• Help create data analysis and analytics pipelines that improve not only the underlying ML algorithms but also the product and the user's experience. 
• Lead a team of Analysts
• Be responsible for good data-driven hygiene, including the creation, redesign, and maintenance of analytics dashboards and BI tools such as BigQuery, Google Data Studio, or Tableau. 
• Advise on data labeling strategy so that Woebot's conversational engine is continuously improved and informs changes in features, content, and UX. 
• Create and lead a data-driven personalization strategy for this audience. 
• Personalization is key for developing a relationship over time while delivering precision interventions, which are the right method to the right person at the right time. 
• Help develop evidence and trends to demonstrate the value of highest-priority products. 
• Helping answer the most pressing questions in the mental health field around engagement, dose, and treatment outcomes. 
• Contribute to the pipeline of peer-reviewed published research. 
• Lead publication in Data Science and HCI research. 
• Assess the lay of the landscape and team 
• Make suggestions around in-house analytic tools, dashboards and strategies. 

Skills: Data Analysis, Team Leadership, Analytics Pipeline Development, Data Hygiene Practices, Dashboard Maintenance, Data Labeling Strategy, Personalization Strategy, Research Publication

• Be responsible for advanced and predictive data analytics from complex and high-value projects using big data and data science technology for healthcare innovation and outcomes. 
• Be responsible for conducting complex statistical and algorithmic data analyses and the development of technologies to improve the health of members. 
• Lead data analytics challenges in the organization, collecting large volumes of data from varying sources
• Working with Data Scientists to clean and interpret data, create solutions to overcome challenges and communicate with interested parties. 
• Build an environment across Data Scientists who are hungry to stay up to date on the latest technologies and practices. 
• Set the standard of being curious in solving the most complex problems and leading the vision of opportunities for improvements
• Working in a collaborative environment with other Leads, Developers, Product Managers and Experience Designers. 
• Design and develop analysis tools that extract, prepare, and analyze data and store analytical results for downstream analysis and reporting for business needs 
• Build key data sets to empower operational and exploratory analysis and designing and evaluating experiments monitoring key product metrics, understanding root causes of changes in metrics 
• Develop tools to convert unstructured data to partially or fully structured representations as well as process large sets of data to derive statistical information. 
• Apply expertise in quantitative analysis, data mining, and the presentation of data to understand how users interact with business products 
• Develop mathematical and statistical models for distinguishing relevant from irrelevant content or events. 

Skills: Predictive Data Analytics, Statistical Analysis, Data Cleaning and Interpretation, Data Environment Leadership, Collaborative Problem Solving, Analysis Tool Development, Unstructured Data Processing, Quantitative Analysis

• Participate in developing presentations and communicate the results of research analysis and findings. 
• Participate in the design of automated, operational analytics processes to achieve scale and durability of analysis processes. 
• Measure and validate the outcomes of health management programs using SAS, R and other tools, including provider data, claims data, membership data, etc. 
• Coordinate analysis and development initiatives and capabilities with health plans. 
• Manage multiple projects as assigned and assist with training Data Analysts. 
• Be responsible for development efforts with technical team liaisons, including gathering business requirements, documentation, testing, delivery and user adoption
• Communicate expectations to the Health Plan.
• Understand the roadmap with the founder and reverse engineer tailored interventions. 
• Deliver a prioritized list of roadmap items.
• Execute a plan for improving user outcomes with analytics and ML.

Skills: Research Presentation, Automated Analytics Processes, Outcome Measurement and Validation, Project Coordination, Data Analyst Training, Technical Development Liaison, Stakeholder Communication, User Outcome Improvement

• Hands-on development and coding of novel machine learning, statistical analysis, and operations research models.
• Build and prototype analysis pipelines iteratively to provide insights at scale.
• Collaborate with software engineering, product, and operations teams to understand company needs and propose possible solutions.
• Develop comprehensive knowledge of Beacon data structures and metrics, identifying and advocating for changes 
• Serve as the lead data expert and statistician on the APS project.
• Managing the APS data universe, determining areas of error
• Implement strategies for data improvement
• Analyzing, interpreting, and reporting on APS & vendor data regarding data quality and optimization of data translation efficiencies.
• Evaluating new APS vendors and potential data sources (both government and private industry supplied) to advise the Government on data acquisition strategy and cost-to-benefit ratios.
• Participating as the data SME on cross-project strategies involving Provider Screening & Enrollment data
• Providing expertise in screening, enrollment, and claims data to advise the Government on making informed data-driven business decisions.
• Use a variety of industry-accepted tools, standards and methodologies in mathematics and statistics to determine both data quality strategies and analysis.

Skills: Machine Learning Development, Analysis Pipeline Prototyping, Cross-Functional Collaboration, Data Structure Knowledge, Data Quality Management, Data Improvement Strategies, Vendor Evaluation, Statistical Analysis Expertise

• Be a thought partner for the Pricing & Performance Manager and Sales leadership in unlocking the power of data & analytics to better understand markets, customers and business offerings
• Help build the foundation of a strong analytical and data science engine in the pricing and commercial organization
• Support the development and implementation of the pricing strategy
• Support the creation and regular update of Price price-setting algorithm with data insights, and analysis of market data (i.e., competitor data and historical pricing levels, service levels, value insights, etc.)
• Support commercial teams by providing and presenting commercial intelligence and performance insights for decision-making by collecting, organizing and analyzing data from different internal and outside systems
• Perform research and build a core understanding of customer performance metrics to qualitatively inform and interpret models.
• Work on interdisciplinary projects, using mathematics, econometrics, statistics, and machine learning to derive structure and knowledge from raw data across internal and external sources
• Be the process and value owner for pricing tools and platforms.
• Taking the end-to-end responsibility (problem identification, data mapping, data preparation, modeling, and implementation) for data-related projects
• Interact with many stakeholders and a variety of departments, including sales, innovation, marketing, customer service, finance and supply chain
• Utilize Machine Learning and Data Mining to drive innovation
• Champion the team's operations

Skills: Data Analytics, Pricing Strategy, Market Analysis, Commercial Insights, Performance Metrics, Project Collaboration, Tools Management, Machine Learning

• Understanding business challenges and creating valuable, actionable insights
• Performing analysis using statistical and ML methods for predictive analytics, classification, clustering and regression
• Clean, manage, and structure data from disparate sources
• Help transition from development environment to production
• Apply advanced machine learning and Natural Language Processing (NLP) techniques to drive business decisions
• Mentor and help develop team members while keeping the focus on goals and deliverables
• Empower the team and help guide research and development, with senior of novel intelligent approaches and solutions for the Search ecosystem
• Help the business define a Data Science strategy and work on new ways to make the most of data
• Work closely with the engineering team members to integrate machine learning (ML) algorithms into the platform
• Help in the design of new features in the product, and drive innovation inside the company through the use of disruptive technologies
• Help find new talent for building the next generation of Farfetch data scientists

Skills: Business Insight, Statistical Analysis, Data Management, Model Deployment, NLP Techniques, Team Mentoring, Research Guidance, ML Integration

• Help develop vision, business case, modeling spec and operational project plans for an end-to-end ML solution
• Lead the development of data science/ML models for high-impact business opportunities across all business areas
• Work on all stages of a data science / ML project - exploration and conceptualization, POC (proof of concept), data preparation, model development and testing, deployment, monitoring and debugging, continuous improvement
• Train, mentor and guide junior data scientists in the team
• Partner with product, data engineering, and ML engineering teams to ensure seamless production of the developed algorithm and management of the full model cycle
• Develop and follow best practices for developing, managing, maintaining and documenting machine learning models and all related work
• Develop a deep understanding of different business functions and core KPI to ensure that the data science solution fits the business need
• Lead the development of standard building blocks for usage across multiple ML algorithms

Skills: Vision Planning, Model Development, Project Execution, Team Mentoring, Cross-Functional Collaboration, Best Practices, Business Understanding, Algorithm Design

• Leads prototyping as well as supporting pilot programs for R&D purposes
• Creates, maintains, and supports data pipelines across diverse systems (Azure/AWS, Oracle, Mainframe)
• Delivers all analyses to support KPI definition/ implementation, standards and best practices
• Conducts analyses to support Methods programs
• Ensures documentation of the new methodologies and best practices
• Work with cross-functional teams to design, implement, and test new consumer and audience measurement methodologies
• Provide research support for the identification and implementation of methods and best practices to optimize data collection approaches
• Represents Nielsen within academic and research councils

Skills: Prototyping Support, Data Pipelines, KPI Analysis, Method Analysis, Method Documentation, Cross-Functional Design, Research Support, Academic Representation

• Conceptualize, design, and deliver high-quality solutions and insightful analysis
• Conduct research and prototype innovations, data requirements gathering, solution scoping, and architecture
• Perform and deliver independent statistical and machine learning research/projects
• Involve state-of-the-art machine learning algorithms to solve challenging high-impact business problems
• Formulating, suggesting, and managing data-driven projects that are geared toward furthering the business's interests
• Collating and cleaning data from various entities for later use by Junior Data Scientists
• Managing, monitoring, and mentoring Junior Data Scientists in order to facilitate competency development and the successful completion of projects
• Selecting and employing advanced statistical procedures to obtain and suggest actionable insights that might be used to inform business strategies

Skills: Solution Design, Research Prototyping, Independent Modeling, Advanced Algorithms, Project Formulation, Data Preparation, Team Mentoring, Insight Generation

• Cross-validating models to ensure generalizability
• Producing and disseminating non-technical reports that detail the successes and limitations of each project
• Staying informed about developments in Data Science and adjacent fields to ensure that outputs are always relevant and conform to best practice standards
• Understand a problem statement and implement analytical solutions and techniques independently or with minimal supervision
• Discover effective solutions to complex problems
• Break the problem into small problems and think of relevant techniques that can be explored and used to address those
• Learn and pick up a new language/tool/algorithm/platform quickly
• Problem solver with a curiosity for exploring new techniques and technologies in the Analytics, AI and Machine Learning domain

Skills: Model Validation, Report Communication, Industry Awareness, Analytical Execution, Complex Problem-Solving, Technique Exploration, Rapid Learning, Technical Curiosity

• Designing and deploying deep learning algorithms and predictive models
• Develop custom data models and algorithms to apply to data sets
• Assess the effectiveness and accuracy of new data sources and data gathering techniques
• Develop processes and tools to monitor and analyze model performance and data accuracy
• Collaborate with data and subject matter experts throughout the organization to identify opportunities for leveraging data to drive business solutions
• Answer complex business questions by using appropriate statistical techniques on available data or by designing and running experiments to gather data
• Adapt standard or develop novel applications of classification, forecasting, simulation, optimization, and summarization techniques
• Work closely with cross-functional teams to encourage best practices for experimental design and data analysis
• Create compelling interactive visualizations and presentations to enhance decision-making capabilities throughout the company
• Regular, dependable attendance and punctuality

Skills: Deep Learning, Model Development, Data Assessment, Performance Monitoring, Data Collaboration, Statistical Analysis, Technique Adaptation, Data Visualization

• Facilitate and support all ATF data strategies and initiatives
• Utilize methodical approaches to developing data strategies that leverage ATF data to achieve strategic goals
• Develop recommended data governance policies that result in data architectures and data models, which optimize ATF data for strategic and tactical decision making through data analytics and data reporting
• Develop and maintain data architectures that allow ATF to leverage data to achieve strategic goals
• Develop interpretations of the ATF data that can be communicated at an executive level
• Transform strategic data requirements into data models, queries, and data analytics to achieve goals
• Assess the ATF's current business intelligence, data analytics and reporting tools and make recommendations for modifications and improvements
• Conduct in-depth data analysis using data science techniques, utilizing advanced data modeling and analytics approaches
• Analyze, recommend, and guide the implementation of data analysis and management methods that will facilitate machine-learning initiatives

Skills: Data Strategy, Governance Development, Data Architecture, Executive Interpretation, Requirement Translation, Tool Assessment, Advanced Analytics, ML Enablement

• Working with the Head of Department to scope out a vision for the Data Science practice within this multi-national organisation
• Liaising with internal stakeholders across the business to propose a PoC for the function
• Building toward managing a team of Data Scientists
• Collaborate with stakeholders to develop new data pipelines and visualizations
• Develop, deploy, and maintain BI and data modeling tools
• Create technical documentation for BI tools
• Create advanced reports and models by combining multiple data sources to help advise business decisions and strategies
• Create data processes and systems to achieve big picture outcomes
• Translate business requirements into technical ones
• Work with HRIS to perform data audits to ensure data integrity

Skills: Vision Scoping, Stakeholder Liaison, Team Leadership, Pipeline Development, BI Deployment, Technical Documentation, Data Modeling, Requirement Translation

• Collaborate with AO leaders, staff and business stakeholders from clinical, operational, financial and technical areas to gather, define and analyze requirements as well as design and implement robust reporting/analytic solutions to generate clinical, operational and financial insights
• Collaborate and establish working relationships, effective communication strategies (oral and written) across the organization and at different levels, strategic thinking and actions to influence leadership stakeholders and key business decisions
• Design, develop and pilot/ deploy state-of-the-art, data-driven optimization algorithms, forecasting and predictive/descriptive models beyond current PHS analytics capabilities
• Solve business problems using the latest and most appropriate technologies in statistical modeling and machine learning
• Conduct risk identification and mitigation planning and inform analytical senior leadership of any issues/escalations and risks
• Conduct design, modeling, statistical analyses and data visualization for both exploratory and hypothesis-driven data studies using clinical, financial, and administrative data sources
• Develop, test and validate both existing and new analytical prototypes, algorithms, models, methods, and data processes to support business decision making as well as clinical and operational process improvement
• Managing multi-disciplinary projects involving multi-tasking, achieving milestones, deadlines, and maintaining customer relationships

Skills: Requirement Analysis, Stakeholder Communication, Model Development, Machine Learning, Risk Mitigation, Data Visualization, Prototype Validation, Project Management

• Conducting overall program evaluations (i.e., Planning-implementation-completion/analysis-reporting) and supporting other assessments such as change management assessments, workflow optimization assessments, etc.
• Managing healthcare reporting/analytics system implementations throughout the project life-cycle, performing workflow analysis, testing, training, go-live, system upgrades and issues management in partnership with IT
• Analyze and model structured data and implement algorithms to support analysis using advanced data-driven analytical methods from statistics, data mining, econometrics, and operations research
• Perform advanced analytics techniques to mine unstructured data, using methods such as document clustering, topic analysis, named entity recognition, and document classification
• Perform exploratory data analysis, generate and test working hypotheses, and uncover historical trends and relationships using clinical, financial, and administrative data sources
• Support design, test and deployment of optimization algorithms, forecasting and predictive models for patient-level outcomes, utilization management, and overall medical services
• Support design, test and deployment of population scoring, stratification and segmentation based on predicted risk of adverse utilization/clinical and financial outcomes
• Manage and collaborate with other analytics stakeholders to define and pilot prototype BI tools

Skills: Program Evaluation, System Implementation, Structured Modeling, Unstructured Mining, Exploratory Analysis, Model Deployment, Population Stratification, BI Prototyping

• Design and develop reusable analytical assets (data structures, code, solutions)
• Recommend improvements to existing assets
• Evaluates the suitability and value of potential new assets
• Train and manage analytical talent on the appropriate use and interpretation of new analytical models
• Facilitate integration of these models into existing business analyses as well as assure, maintain and improve efficiency and quality
• Validate the work product of peers and junior scientists
• Established track record of presentations/publications and thought leadership in the AO area
• Contribute to developing a year-end value story to demonstrate the value of the team and their contribution to progress towards the AOs and PHS goals

Skills: Asset Development, Process Improvement, Asset Evaluation, Talent Training, Model Integration, Work Validation, Thought Leadership, Value Demonstration

• Develop innovative approaches to measuring user behavior and evaluating performance in the virtual environment
• Collaborate with partners such as Engineering and Solutions teams to instrument the data collection process and build ETL pipelines to obtain data required for analyses
• Implement approaches and communicate insights in a compelling narrative
• Incorporate research outcomes into the data product offering
• Design and implement the machine learning and deep learning algorithms
• Lead and grow a team of machine learning practitioners
• Work on cutting-edge inference problems involving a wide variety of data about retailers, brands and products to power rigorous decision-making and help customers succeed
• Develop methods and tools that increase the rigor and efficiency of experimentation platforms and analyses that are constructed using causal inference techniques
• Write and collaborate on production code with engineers to implement new inference methods and improve experimentation-related pipelines
• Consult with other team members across Faire on complicated experimental designs and on analyses estimating causal effects
• Stay aware of academic literature and industry best practices around experimentation and causal inference and be a champion of good statistical practices at Faire

Skills: User Measurement, Data Engineering, Insight Communication, Product Integration, Algorithm Design, Team Leadership, Causal Inference, Experimentation Tools

• Working as a Lead Data Scientist in a highly collaborative environment 
• Leveraging cloud data collaboration tools like Databricks, to explore, prototype and build models in collaboration with the team (other Data Analysts and Data Engineers)
• Working with big data in the cloud, using analytics engines and cluster computing frameworks like Spark, MapR, Tez and Yarn
• Providing technical guidance and mentorship for less-experienced team members 
• Guiding and participating in the building of data sets
• Working extensively with R and Python / Python libraries to extract and manipulate data from a variety of internal and external sources (Scikit-learn, Pandas, Numpy, Scipy, PySpark, NLTK)
• Creating and deploying various machine learning algorithms and tuning models to achieve desired outcomes
• Conducting deep analysis on business topics related to customer sentiment analysis, customer segmentation analysis, risk and fraud, etc.
• Running predictive analytics to gain insights into consumer behaviors 
• Using data visualization software to effectively redact and communicate data outcomes and recommendations to senior leadership (using Tableau, RShiny, or similar tools) 
• Presenting outcomes and targeted recommendations to Directors and C-Suite executives 

Skills: Cloud Modeling, Big Data, Technical Mentorship, Dataset Building, Python Programming, Model Tuning, Predictive Analysis, Data Visualization

• Design features and build large-scale machine learning models to detect sensor events (emptying, on fire, maintenance required, etc.) and predict when it’s the ideal time to empty containers to avoid overflowing and emptying of half-empty containers
• Work together with backend engineers, hardware engineers, testers, and fellow data scientists to design and implement the infrastructure needed to make modeling, testing, and deploying models smooth
• Work with other data scientists on the team to develop both short and long-term strategies around machine learning for forecasting
• Collaborate with the hardware and software teams to tune and integrate the solutions developed
• Develop and maintain internal monitoring and reporting tools to enable being proactive and demonstrating value to customers
• Build internal tools for reducing the installation team’s turnaround time for assisting customers
• Build up the Machine Learning and Experimentation platform for data scientists to work with
• Implement data pipelines that allow for versioning and implementations of the training datasets
• Collaborate with, mentor, and influence members within the team to drive the development process to enable data scientists to deliver their best work
• Write clean, delicious, tested, and maintainable code, and peer review team members’ code

Skills: Feature Engineering, Model Deployment, Forecast Strategy, Solution Integration, Monitoring Tools, Pipeline Development, Team Mentoring, Clean Coding

• Define and support the research and analytical process that will deliver rigorous business insights
• Responsible for advanced statistical and machine learning modeling in various areas of the business
• Develop data-driven analytical tools to make the results of models available to users
• Build models that can be used reliably for automation of the business
• Present findings to senior management to drive the decision-making
• Mentor the team of data scientists and analysts
• Responsible for the success of other data scientists and analysts, leading by example
• Responsible for communicating the impact to the other teams and stakeholders
• Resource planning and allocation to the projects

Skills: Insight Development, Advanced Modeling, Analytical Tools, Model Automation, Executive Presentation, Team Mentoring, Impact Communication, Resource Planning

• Leading a talented team of Data Scientists
• Building and maintaining a complex analytical algorithm using Python and AWS
• Leading 2 Data Science teams of 6 people
• Creating and delivering Data Science projects regularly
• Effectively collaborating with colleagues to solve business problems
• Building high-impact insights, models and recommendations for the business, using Python, R, AZURE/AWS
• Deploying and maintaining Complex Analytical Algorithms
• Recruit, coach and support the development of the team
• Provide technical leadership to the team
• Identify and evaluate emerging, open source, machine learning libraries and technologies
• Work with external partners, such as Universities, to develop and test novel ML models and solutions to business problems

Skills: Team Leadership, Algorithm Development, Project Delivery, Cross-Functional Collaboration, Insight Modeling, Model Deployment, Talent Development, Technology Evaluation

• Develop, automate and maintain analysis ranging in complexity from basic application utilization to complex outcomes evaluation analysis
• Apply simple and complex mathematical models to sniff out and test for the effects of theories and measure the impact of digital interventions in the real world
• Communicate findings and new capabilities to a wide range of stakeholders, from CEO's and Presidents to low-level engineers and developers
• Deploy a developed model onto a server or in a cloud-based data architecture, tailoring implementation methods to match the scale, speed, and complexity of the problem being tried to solve
• Work towards the goal of creating a cross-functional team capable of working from a Kanban
• Utilizing agile software development rituals to align closely with dev teams and deliver value through integrating data-derived insights and capabilities into the application
• Work cross-functionally with business managers/product managers/engineers, and designers
• Develop predictive analytics for customer behaviour and apply machine learning, optimisation techniques in core subject areas
• Build, validate, test, and deploy models and algorithms
• Implement a data science experimental framework to enable organization-wide experiments
• Make a strategic data architecture recommendation
• Manage and guide Junior Data Scientists in the team

Skills: Analytical Automation, Mathematical Modeling, Insight Communication, Model Deployment, Agile Collaboration, Cross-Functional Work, Predictive Analytics, Team Guidance

• Uses AI, machine learning/deep learning to build neural networks to create improved clinical monitoring, data analytics, clinical communication and smart devices to support clinical decision making and improve patient care
• Interacts with engineers, clinical consultants, and business partners to identify questions and issues for data analysis and experiments related to specific clinical problems (e.g., patient deterioration, falls, pressure injuries, sepsis, vital sign interrogation, etc.)
• Identifies unique opportunities to collect new data from existing sensors
• Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions
• Verifies model effectiveness based on laboratory, simulated, and real-world data
• Identifies meaningful insights from large data and metadata sources
• Identifies, interprets and communicates insights, trends, and patterns from analysis and experiments to key stakeholders/decision-makers
• Engage with customers to understand future needs, brainstorm solutions, develop and test new concepts, and develop clinical and economic evidence in support of new solutions
• Develops strategies for the effective management of Protected Health Information and non-PHI data streams on medical devices, on-premises servers and cloud-based storage
• Creative, open-minded problem solver, able to function effectively as part of a small team with an agile mindset

Skills: Neural Modeling, Clinical Analysis, Data Acquisition, Big Data Engineering, Model Validation, Insight Extraction, Stakeholder Communication, Agile Problem-Solving

• Actively engage with internal business teams to understand industrial challenges and deliver robust, data-driven solutions
• Lead ongoing analytics delivery of currently deployed products, live with field
• Work alongside global counterparts to solve data-intensive problems using standard analytical frameworks and tools
• Network and collaborate with a broad range of internal business stakeholders to define and deliver joint solutions
• Leverage cutting-edge technology to creatively solve problems and disrupt existing business models
• Leading a team and setting best practices to develop programming and analysis skills of self and others
• Working with customers and other digital professions to convert business needs into data-driven solutions, meeting stakeholders’ strategic goals
• Confidently communicating technical and analytical findings and recommendations that inform wider decisions to senior audiences, using media tailored to the audience
• Understanding the benefits of applying data science techniques to analyse data, provide insights and solve problems in a real-world setting and developing strategies for increasing their adoption 
• Understanding software and data engineering essentials, including producing repeatable pipelines, version control, testing strategies and performance optimisation
• Demonstrating a deep understanding of data privacy, data protection and the ethical implications of data science

Skills: Business Engagement, Analytics Delivery, Data Frameworks, Cross-Team Collaboration, Creative Technology, Team Leadership, Solution Translation, Technical Communication

• Interact with business groups to understand critical business problems
• Identify and drive broad research directions using AI/ML
• Manage and mentor junior data scientists to create cutting-edge data-driven systems and technologies that can considerably impact the business outcomes
• Contribute to the future vision and innovation needed for the growth of the organization
• Use of cutting-edge Analytics techniques for building advanced prediction and recommendation systems, knowledge mining, insight generation, and business automation
• Understand business challenges and goals to formulate the approach for data analysis and model creation that will support business decision-making
• Research on innovative technologies to come up with novel data mining, modelling/segmentation approaches required to solve unique and complex business problems
• Create publications and patents from them
• Interpret, document and present/communicate analytical results to multiple business disciplines
• Providing conclusions and recommendations based on customer-centric data
• Coach and mentor Data Scientists

Skills: Business Understanding, Research Direction, Team Mentoring, Strategic Innovation, Advanced Analytics, Problem Formulation, Novel Modeling, Insight Communication

• Produce innovative solutions driven by exploratory data analysis from unstructured, diverse datasets
• Applies knowledge of statistics, machine learning, programming, data modeling, simulation, and advanced mathematics to recognize patterns
• Identify opportunities, pose business questions, and make valuable discoveries leading to prototype development and product improvement
• Uses a flexible, analytical approach to design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from the data
• Analyze large amounts of data and identify anomalies, recognize patterns, and provide business-level insights
• Work with multiple sources of data, data structures, pipelines and effectively form a combined dataset powerful enough for modeling
• Identify the scopes of applying Machine Learning strategies
• Explore various model options that best suit the problem
• Design algorithms to make them robust
• Convert insights to descriptive, predictive, and prescriptive models by creating optimal solutions
• Query large datasets with the ability to construct the right data structures by analyzing their complexity

Skills: Exploratory Analysis, Pattern Recognition, Business Discovery, Predictive Modeling, Data Integration, ML Strategy, Algorithm Design, Data Querying

• Develop, test, and evaluate up-to-date Machine Learning and Deep Learning models
• Automate processes for predictive model validation, deployment, and implementation
• Establish an advanced analytics and evaluation system about the latest ML technologies
• Contribute to the high-quality documentation of data definitions, transformations, and processes to ensure data security, governance, and transparency by creating well-documented data with defined dataset quality, uptime, and usefulness
• Work as part of a team of problem solvers, helping to solve complex business issues from strategy to execution
• Participating in technical discussions to discuss emerging ML technologies and implement optimal solutions
• Promote and propagate a data-driven culture
• Reviews the work of others, develops innovative approaches
• Serves as a leader/mentor for the other data scientists

Skills: Model Development, Process Automation, Analytics Systems, Data Governance, Problem Solving, Technical Evaluation, Data Culture, Team Mentoring

• Work with large datasets to solve challenging problems using advanced statistical and Machine Learning techniques
• Prototype and productionize Machine Learning pipelines to provide a solution to large commercial impact problems that keep Farfetch ahead of the competition
• Collaborate with other Scientists and Engineers to build efficient data products
• Identify opportunities for design and assess improvements to Farfetch products
• Research and develop forecasting and recommendation systems to improve the quality of key services powering the Private Client function, leveraging computer vision, ranking, predictive modeling, and live experiments
• Lead and manage more junior Data Scientists, aiding their growth and developing them to the next level
• Act as an ambassador for the Farfetch Data team, supporting recruitment efforts and promoting a Brilliant working environment
• Train machine learning and deep learning models on a computing cluster to perform visual recognition tasks, such as segmentation and detection
• Develop state-of-the-art algorithms in one or all of the following areas: deep learning (convolutional neural networks), object detection/classification, tracking, multi-task learning, large-scale distributed training, multi-hypothesis testing and model building
• Optimize deep neural networks and the associated preprocessing/postprocessing code to run efficiently
• Prepare data for building the ML-based models

Skills: Large-Scale Modeling, Pipeline Production, Data Product Collaboration, Product Improvement, Forecasting Systems, Team Leadership, Model Training, Algorithm Optimization

• Design, build and improve machine learning models
• Work end-to-end from data collection, feature generation and selection, algorithm development, forecasting, visualization and communicating model results
• Collaborate with engineering to productionize models
• Drive experimentation to test the impact of model-based optimization
• Develop a comprehensive understanding of subscriber and payment data structures and metrics
• Mine large data sets to identify opportunities for driving growth and retention of subscribers
• Develop prototype solutions, mathematical models, algorithms, and robust analytics leading to actionable insights communicated clearly and visually
• Partner closely with business stakeholders to identify and unlock opportunities, and with other data teams to improve platform capabilities around data modeling, data visualization, experimentation and data architecture

Skills: Model Development, End-to-End Processing, Model Production, Experimentation Design, Data Understanding, Opportunity Mining, Prototype Analytics, Stakeholder Partnership

• Research, development and evaluation of state-of-the-art deep learning and machine learning algorithms
• Development and implementation of Data Science and AI/ML workflows geared towards extracting actionable information from time series data
• Set up of continuous learning environments for optimization of AI-based models concerning robustness and stability
• Work closely with product and clinician teams to plan projects and structure work in an Agile development process
• Document ML-based systems and facilitate architectural reviews of projects
• Identify areas of innovation, formulate research hypotheses, and rapidly prototype new models
• Presenting a solid understanding of Python and/or R and associated tools/IDEs (e.g., TensorFlow, scikit-learn, numpy)
• Derive, interpret and effectively deliver the results of data analysis using visualization techniques, tools, custom applications, or by narrating stories about the solutions to business problems
• Understand traditional and new data analysis methods that can build statistical models and discover patterns in data
• Work directly with business stakeholders and engineering teams, ask the right questions to create high-quality solutions that solve end-user problems
• Utilize a broad range of techniques in machine learning, data mining, statistics, and big data interrogation methods
• Provide work estimates and participate in design and implementation reviews
• Develop and execute agile work plans for iterative and incremental project delivery

Skills: Algorithm Research, Time Series Modeling, Continuous Learning, Agile Planning, System Documentation, Rapid Prototyping, Data Visualization, Stakeholder Collaboration

• Create new insights that will drive business growth and profitability in the short and long term
• Work effectively as a part of a team and as an individual to do research and develop cutting-edge and effective solutions for the business
• Provide technical guidance and help advance the team’s data science talents
• Provide expertise in analyzing and visualizing large, complex, and unique data sets
• Deploy analytics code with a continuous integration environment incorporating version control, build servers, and test servers
• Manage analytical projects and assist in program planning 
• Work closely with IT teams to identify issues and use data to propose solutions for effective decision making within the IT domain (cyber security, network performance, application monitoring and stability, incident management, to name a few)
• Use machine learning tools and statistical techniques to produce solutions to problems
• Test and validate model performance
• Provide SME support in the use of data science tools to both the business and IT community, driving out efficiency, capability and best practice
• Work with tooling vendors to get the most out of product features and inform product roadmaps

Skills: Business Insight, Technical Leadership, Data Visualization, CI Deployment, Project Management, IT Collaboration, ML Solutions, Model Validation

• Collaborate with stakeholders throughout the organization to develop project plans for delivering objects and timelines for risk model development and implementation
• Implement and develop risk models in Python for regulatory stress testing submission and company risk management
• Design and build the execution workflow of models to forecast Balance Sheet, Fee Revenues, Macroeconomic Factors, Expense and calculate risk metrics under various stress scenarios, sensitivity and attribution analysis
• Coordinate with business quants and functional users to implement models and coordinate coding, testing, implementation and documentation of financial models for CCAR/CECL, including credit risk, market risk, RWA and balance sheet forecasting models
• Develop processes and tools to monitor and analyze model performance to ensure the expected application performance levels are achieved
• Execute enterprise standards for model validation by applying statistical techniques and methodologies to test assumptions and review the results of models
• Develop presentation decks using visual analytics tools and techniques (JupyterHub/Python)
• Apply data mining, data modelling and machine learning techniques to analyze large financial datasets and enhance the model performance
• Working with market risk, credit risk or treasury risk models and financial products

Skills: Project Planning, Risk Modeling, Workflow Design, Model Implementation, Performance Monitoring, Model Validation, Visual Analytics, Financial Analysis

• Build and train production-grade ML models on large-scale datasets to solve business use cases
• Use large-scale data processing frameworks to manipulate and extract value from both structured and unstructured data
• Use Deep Learning models like NLP for solving various business use cases involving forecasting and anomaly detection
• Perform data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause-and-effect relations
• Create data models using best practices to ensure high data quality and reduced redundancy
• Stay current on industry trends and identify opportunities to adopt the latest methodologies into existing implementations
• Market and present proposed solutions to Sr. Business and Technology colleagues
• Drive the development, testing, and implementation of new measurement methodologies, including client-focused impact assessments
• Build and deploy production-ready automated methodologies into functional modules, including unit tests and quality control checks, while incorporating CI/CD workflows
• Identify root causes and solve any bugs or quality escapes
• Ensure detailed documentation of new methodologies, research findings, and best practices
• Ensure product quality, stability, and scalability by facilitating code reviews
• Explaining complex model-related concepts in simple terms to non-technical internal and external audiences
• Mentor and provide oversight to junior associates while serving as an example by sharing relevant best practices

Skills: Model Training, Data Processing, Deep Learning, Experiment Analysis, Data Modeling, Trend Adoption, Solution Presentation, Quality Assurance

• Partners closely with IT, business, and data management/engineering teams to understand, utilize, and improve data infrastructure
• Advises and serves as an objective and transparent partner to drive fact-based decision making and a measure of success culture
• Responsible for a solid knowledge of consumer analytics including closure models, retention models, agency economics, and lead optimization
• Hands-on ability with advanced programming, complex ETL and specialized modeling methods to drive home projects and lead the team through examples of good technical skills
• Manages model deployments via MLOps techniques
• Works with analytics and IT teams to deploy models/rules in platforms like Algorithmia
• Help steer MLOps environment improvements
• Makes complex technical material simple to non-technical people
• Manages a team of data science professionals

Skills: Data Infrastructure, Decision Support, Consumer Analytics, Technical Expertise, Model Deployment, MLOps Improvement, Technical Communication, Team Management

• Lead and provide strategic direction to a team of Data Scientists and Analysts / Data Scientists to deliver quality analysis and insight to answer strategic and tactical questions for Asset Operations
• Promote the use of Data Science to support the Senior Asset Reliability Improvement Manager in delivering the Data and Analytical strategy set up for Asset Operations
• Guide his/her team on data science methodologies and approaches, by assuring the quality of the output and analytic methodologies used
• Overlaying large and complex datasets from different systems/sources to test hypotheses and/or discover hidden relationships and useful information
• Use of advanced statistical analysis techniques including data mining, feature engineering and selection, and supervised and unsupervised machine learning, to extract the maximum intelligence from data
• Communicate the intelligence extracted from this analysis to key stakeholders in a simple way, focusing on business value and giving clear recommendations
• Champion of the development and usage of predictive analytics within the team and business 
• Develop and test algorithms that can use existing data feeds to categorise asset behaviour and thus improve the management of that asset
• Give feedback on the quality and structure of datasets and how this could be improved to enhance predictive analytics capabilities
• Identify potential new data sources and evaluate/apply emerging technologies for data discovery and analytics usage
• Identify new opportunities to drive the innovation agenda across the business
• Create strategies to overcome barriers to change to ensure the benefits of data science in Asset Operations are recognised and exploited to deliver value

Skills: Strategic Leadership, Data Strategy, Methodology Guidance, Data Integration, Advanced Analytics, Insight Communication, Predictive Modeling, Innovation Development

• Experience providing advanced analytics solutions
• Prior experience working with large quantitative and qualitative datasets
• Experience with programming languages such as Python, R, and VBA, and query languages such as SQL
• Experience with data visualization tools (e.g., Tableau, Qlik, Power BI)
• Proficiency with data extraction, transformation, and loading to support advanced analytics
• Experience with a wide range of analytics techniques, such as statistics, machine learning, natural language processing, optimization, simulation, or closely related techniques
• Ability to use appropriate methodology and approach for data science solutions to business problems
• Familiarity with cloud computing technology
• Must have strong collaboration skills
• The ability to excel in a fast-paced environment

Qualifications: BS in Mathematics with 4 years of Experience

• Prior banking or financial services experience.
• Knowledge of Actimize fraud monitoring solutions or equivalent platforms
• Excellent programming skills in one of the following tools - Python, R, SQL, and other tools.
• Knowledge of Tableau or PowerBI, able to visualize results using data visualization tools to make your conclusions more understandable to your audience.
• Analytical aptitude, critical and strategic thinking.
• Able to determine the optimal analytic and strategic approach for the development of Fraud strategies to mitigate fraud loss and improve fraud detection processes.
• Able to analyze results and make recommendations for key business partners and senior management or communicate conclusions from complex strategic solutions to a wide range of audiences.
• Knowledge and experience with ETL, Data warehouse, Hadoop, Teradata, Tableau
• Excellent written and verbal communication, business analysis, and consultancy skills
• Strong interpersonal skills with the ability to lead client projects and establish requirements in non-technical language.

Qualifications: BS in Computer Science with 5 years of Experience

• Combined data science and engineering experience with at least three of those on a machine learning team, with a track record of putting models into production and growing individual responsibilities
• Must be expert-level SQL
• Insatiable curiosity and analytical bent 
• Strong Python experience - experience writing code in a production environment
• Expert-level knowledge of data science libraries such as pandas, numpy, tensor-flow/keras or pytorch, sci-kit-learn, plotly (or equivalent)
• Strong familiarity with a gamut of data science and machine learning approaches, including neural networks, random forest and GBTs, KNNs, k-means clustering, various types of regressions, and Bayesian techniques
• Ability to function in a “big data” environment including an intermediate to expert knowledge of Spark or the ability and interest to quickly learn it on the job
• Familiarity with the AWS ecosystem and tools such as Glue, SageMaker, Athena, and Fargate
• Familiarity with Docker or other container systems
• Intelligence, humility, candor, and the ability to thrive in a fast-paced environment

Qualifications: BS in Applied Mathematics with 7 years of Experience

• A strategic and innovative thinker who is agile and possesses leadership capability.
• Experience with SQL and database skills in Teradata, and/or SQL Server databases.
• Strong Microsoft Office skills --Word, Excel, PowerPoint, Visio.
• Proficiency in R, Python and/or other complex analytical tools
• Experience in training and mentoring junior members of the team.
• Experience in analyzing data and providing recommendations.
• Client service and/or consulting experience
• Ability to infer complex code and make changes as necessary to adjust to the changing business needs.
• Ability to validate data and verify data marts built to adhere to business needs.
• Experience with both technical and non-technical presentation of analytic results.

Qualifications: BS in Data Science with 6 years of Experience

• Strong experience working with Python or R programming languages
• Experience working with data manipulation, analytic/business insight tools, and data visualization experience
• Experience with SciPy stack or Tidyverse packages
• Experience constructing and performing complex database search queries
• Experience/knowledge of computer science concepts, data architecture, and experience/knowledge of statistics
• Demonstrated experience using statistical and machine learning techniques including supervised and unsupervised learning and natural language processing
• Experience /familiarity with frequentist statistics and probability including predictive modeling
• Must have strong attention to detail.
• Ability to work effectively with cross-functional teams to perform planning and execution of initiatives.
• Ability to be flexible in a changing work environment,  and work well under pressure
• Experience working with digital marketing tools like Adobe Audience Manager, Liveramp and Invoca 

Qualifications: BS in Statistics with 8 years of Experience

• Hands-on skills in industry-standard machine-learning stack (pandas, numpy, sklearn, Keras, Spark)
• Experience in implementing ML models, data pipelines and testing frameworks for production use
• Working experience in analytics, data mining, and/or predictive modeling, and data interpretation
• Programming knowledge and a skill for coding using either R or Python or SQL, and have experience with ML libraries and frameworks
• Excellent understanding of a range of analytics, data science and machine learning techniques and associated technologies and how to optimise
• Strong knowledge of modelling best practice and tooling including delivering in a coherent, controlled way into a range of production environments
• Demonstrable experience of keeping in touch with developments in data science and technology, with an understanding of ways to use and embed in processes
• Ability to work collaboratively with other team members and areas of the business, empowering them to learn and deliver new ways of working
• Experience analyzing raw clickstream data coming from a smartphone/desktop application
• Strong applied mathematics background, building a working knowledge of clinically sound modeling and analysis techniques
• Strong knowledge of statistical programming languages/tools, such as R, Python, SAS, SPSS, Stata, etc.
• Strong experience in SQL and analyzing big data, utilizing multi-threading/parallel processing to process large datasets in memory (tera- or petabytes) 
• Strong knowledge of databases, tools, libraries and programming languages like Hadoop, GCP, Dash, AWS, Spark, SQL, NOSQL, Hive, d3.js, Tableau, Adobe, Java and node.js.

Qualifications: BS in Data Science with 7 years of Experience

• Good Knowledge of Data Science, statistical analysis and data mining
• Ability to work with large, complex and uncleaned datasets 
• Proven knowledge of statistical software packages (e.g., R, Python with pandas/sklearn, etc) 
• Awareness of commercial and financial processes within a large organisation 
• Ability to lead and motivate a medium-sized and highly analytical team 
• Ability to translate complex statistical findings or solutions into simple terms for digestion by key stakeholders, and convert business issues into statistical challenges for the team to answer
• Expert degree of competence in working with large volumes of data and information from various sources, both structured and unstructured 
• Proficient at formulating and testing hypotheses from raw datasets and drawing statistically meaningful conclusions 
• Ability to design robust and complex classification algorithms that can meet a business requirement using machine learning techniques 
• Experience (post-degree) in data science, with substantial exposure to statistical analysis and algorithm building using large and complex datasets
• Experience working as a data scientist (or equivalent) in a commercial organisation
• Experience of asset performance modelling, analysis and reporting within large, complex organisations 
• Experience of managing and mentoring junior analysts or data scientists 

Qualifications: BS in Statistics with 8 years of Experience

• Experience with statistical analysis tools (e.g., Python, R, SAS)
• Experience with Python and Spark for data manipulation
• Demonstrated expertise in developing analytical dashboards using QlikSense, Tableau, and similar products
• Able to turn data into insights
• Designing the most effective charts/views to present information for clarity and proper understanding
• Experience preparing presentations for management/clients that present insights clearly and efficiently
• Strong organizational skills and motivation
• Proven ability to develop and execute work plans and work independently towards aggressive goals and milestones
• Ability to multitask and make progress towards multiple tasks in parallel
• Strong communication and collaboration
• Ability to clearly articulate results of analyses, in writing and verbally, and to work effectively with all levels within the organization
• Excellent inter-personal skills, able to work with colleagues remotely as well as in-person
• Strong Interest and coursework (or demonstrated track record) in data analysis and/or visualization

Qualifications: BS in Artificial Intelligence with 6 years of Experience

• Experience working with big-data database platforms to manipulate and extract information for analysis (e.g., Hadoop, Netezza, Oracle)
• Experience writing complex queries to efficiently extract and manipulate large amounts of data
• Ability to efficiently manipulate large amounts of data to conduct analysis
• Experience using Hadoop clusters 
• Comfortable working with multiple languages and operating systems
• Familiarity and experience with UNIX
• Proven ability to learn and master new subject matter areas
• Ability to think outside the box and constantly strive for more accurate and efficient ways to perform tasks
• Experience developing new products/services
• Ability to combine knowledge across multiple disciplines to solve problems

Qualifications: BS in Bioinformatics with 5 years of Experience

• Working experience in delivering analytics solutions in the supply chain and manufacturing areas
• Strong business acumen with domain knowledge in supply chain management and experience in supply chain modeling
• Extensive experience in optimization and simulation model development related to supply chain and manufacturing with commercial optimization solvers (ex., CPLEX, Gurobi), commercial SC optimization software (ex., AIMMS, LLamasoft) and simulation software (e.g., Anylogic, Arena)
• Expert level of proficiency in machine learning and experience in applying it to supply chain diagnostics and predictions
• Experience in business process, demand and supply planning algorithm implementation with advanced planning systems, e.g., Blue Yonder / JDA, o9, OMP, Kinaxis, SAP APO and SAP IBP 
• Expertise in programming (e.g., Python, Java), relational (e.g., SQL, PostgreSQL), graph database (e.g., Neo4j, Tigergraph) and machine learning with Python, R, Scikit
• Excellent problem-solving, project leadership, interpersonal and communication skills, combined with a business attitude
• Ability to rapidly learn new problems and to work in a matrix environment
• Fluent in English, both written and spoken

Qualifications: BS in Applied Mathematics with 3 years of Experience

• Professional work experience in Statistics, Data Science, Computer Science, and/or related disciplines
• Experience with developing and automating methodologies
• Mastery of Python
• Experience with sophisticated data mining and modeling solutions and the tools that support them such as Scikit-learn, PyTorch, and PyMC3
• Experience with relational and distributed database systems with expertise in tools such as Spark, Presto, and other SQL-based querying engines
• Experience with ETL frameworks like Airflow
• Familiarity with DevOps tools and CI/CD workflows including Git
• Experience working in cloud-based environments such as Azure, AWS, or GCP
• Professional experience in a data scientist/analyst role
• Proficiency in statistics/analytics/visualization tool of choice, including Python, SQL, and PowerBI
• Experience with digital product development
• Excellent communication, organizational transformation, and leadership skills
• Demonstrated excellence in Data Science, Business Analytics and Engineering
• Working knowledge of git, Snowflake, and distributed compute platforms

Qualifications: BS in Mathematics with 7 years of Experience

• Experience related to data and analytics
• Experience in a hands-on data science role, delivering and managing end-to-end advanced analytics solutions
• Expert knowledge of ML development tools/libraries in Python, with the ability to deep dive into the code and mentor others on best practices
• Master software development best practices and have experience operating in an agile/DevOps framework
• Experience in delivering scalable ML/AI cloud solutions using Docker/Kubernetes
• Proven knowledge of advanced statistical modelling and analysis
• Experience, or interest and natural leadership for technically leading a data science team
• Experience with advanced analytics solutions deployed on Microsoft Azure (data lake, cloud DWH, Databricks)
• Good experience working with SQL and NoSQL databases
• Strong communication skills in both French and English

Qualifications: BS in Electrical Engineering with 4 years of Experience

• Research experience in a business setting
• Outstanding statistical and analytical skills with expertise in machine/ deep- learning techniques
• Proficiency in Python
• Experience with modern machine learning libraries and frameworks (e.g., LightGBM, XGBoost, PyTorch, TensorFlow, Stan, etc.)
• Experience with cloud environments and big data technologies (e.g., Spark, AWS)
• Collaborative code development experience, including version control, unit/integration testing, code review and sharing (Git)
• Excellent communication skills
• Knowledge of Natural Language Processing
• Knowledge of Bayesian inference and probabilistic programming (e.g., Pyro)
• Experience with MLFlow
• Knowledge of data visualization tools/packages
• Experience working within Agile teams

Qualifications: BS in Bioinformatics with 5 years of Experience

• Proven experience of predictive modelling and classification techniques including regression, neural networks, random forests and clustering
• Knowledge and experience of at least one of R or Python and their statistical and machine learning libraries
• Knowledge and experience of at least one of SAS or SQL
• Experience creating and using advanced machine learning algorithms and statistics such as regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
• Experience analyzing data from 3rd party providers such as Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, etc.
• Experience with distributed data/computing tools such as Map/Reduce, Hadoop, Hive, Spark, MySQL, etc.
• Experience visualizing/presenting data for stakeholders using such as Periscope, Business Objects, D3, etc.
• Strong technical skills in Python, SQL, R, and Hadoop
• Experience in Tableau or other visualization software
• Ability to translate findings to an executive audience through use of slides, charts, tables, etc.
• Strong Machine learning skills, e.g., Regression, Classification, Clustering
• Ability to employ ingenuity and creative data analysis within a robust statistical framework
• Ability to work in a highly collaborative manner with a reasonable degree of independence

Qualifications: BS in Data Engineering with 8 years of Experience

• Experience in data analytics, organizational consulting, and experience measurement
• Working experience in project and/or people leadership
• Passionate about contributing to an organization focused on continuously improving associate and customer experiences
• Successful demonstrated experience in working with senior executives to develop and drive segment or enterprise-wide functional strategies
• Experience in using mathematics, statistics, modeling, business analysis, and technology to transform high volumes of complex data into advanced analytic solutions
• Ability to exercise independent judgment and decision-making on complex issues regarding job duties
• Expertise in organizational theory and employee behavior
• Advanced proficiency in psychological measurement, e.g., scale development and validation
• Advanced proficiency in statistical methods such as regression, factor analysis, structural equation modeling, natural language processing, etc.
• Experience with Microsoft PowerBI or other interactive data presentation/analysis applications
• Knowledge/experience working with SQL, Python, R, SAS, SPSS syntax, etc., to compile and analyze data

Qualifications: BS in Business Analytics with 7 years of Experience

• Commercial experience as a Data Scientist
• Competency in either Python or R as well as SQL 
• Prior insurance industry, ideally with claims experience 
• Experience with Deep Learning or NLP
• Ability to read, write, speak and understand English
• Expert-level skills and experience with analysis language such as R and/or Python (including relevant packages) in support of advanced analytics
• Advanced-level skills with one or more scripting, analysis, or ETL languages 
• Ability to rapidly learn new languages or techniques
• Broad experience and solid theoretical foundation of the properties of the major families of machine learning models (regression, decision trees, clustering, SVMs, neural networks)
• Able to communicate complex statistical concepts and explain the importance and relevance of data (including communications of results and recommendations) to a wide variety of audiences
• Strong synthesis and presentation skills
• Basic understanding of data architecture, data warehouse and data marts
• Extensive experience with large data sets and the tools to obtain, transform, and store data on Big Data and streaming services

Qualifications: BS in Cognitive Science with 6 years of Experience

• Hands-on mathematical modeling experience in a business environment
• Proficiency in Python / R or other statistical packages
• Advanced knowledge of statistical and data mining techniques (regression, decision trees, clustering, neural networks, etc.)
• Proven experience in working with large datasets and relational databases (SQL)
• Knowledge of additional programming languages (C++, Java)
• Distinctive communications skills and the ability to communicate analytical and technical content in an easy-to-understand way
• Intellectual curiosity, along with excellent problem-solving and quantitative skills
• Ability to disaggregate issues, identify root causes and recommend solutions
• Proven leaders with the ability to inspire others, build strong relationships, and create a true followership, result-driven achievers
• Strong people skills, team-orientation, and a professional attitude

Qualifications: BA in Applied Economics with 5 years of Experience

• Familiarity with massive data sets and tools to deal with them (BigQuery/AWS/Hadoop, etc)
• Experience in behavioural psychology/economics and experimental design
• Experience in predictive analytics, segmentation, and related areas
• Software engineering skills in Java and other languages (R and Python)
• Skills with standard reporting tools such as Looker
• Ability to mentor, guide and inspire colleagues and drive best practices across the company
• Ability to recognise dependencies, build relationships and influence others
• Ability to understand the problems and issues games and business want to solve, and then define the right data, analysis or interpretation to lead to the right recommendations and decisions
• Ability to write complex SQL queries to analyse databases with 300+ million players and work with relational database systems
• Understanding the basics of appropriate statistical or machine learning techniques

Qualifications: BS in Systems Engineering with 4 years of Experience

• Experience with Causal Inference
• Comfortable building and maintaining packages/APIs
• Ability to consult with other data scientists to set up and solve problems
• Willing to conduct methodology research and rapidly experiment
• Experience with both Python and R 
• Experience in consulting or professional services
• Experience in extracting insights from large databases, translating market research into actionable solutions and presenting findings and recommendations to clients
• Experience using advanced algorithms, programming languages, or technologies in the development of technical analytics solutions or capabilities
• High level of independence, able to make time-sensitive decisions rapidly and solve urgent problems without escalation
• Excellent communication skills, particularly on technical topics
• Strong time and project management skills
• Ability to balance multiple simultaneous work items and prioritize 
• Comfort with conducting methodology research, ideally in the Causal Inference space
• Ability to rapidly iterate methodology enhancements/research and provide results in a scientific framework

Qualifications: BS in Data Science with 7 years of Experience

• Experience as a Data Scientist building data solutions incrementally, integrating and managing datasets from multiple sources
• Practical experience with ML in research and development projects
• Understanding of classical and deep learning-based ML methods (CNNs, DL Auto-encoders, etc.)
• Knowledge of relevant analytics, visualization and ML libraries (SciPy/NumPy, Pandas/Matplotlib, Keras/TensorFlow, PyTorch)
• Solid programming experience in Python, R or Julia, experience in other high-level languages (C/C++/C#/Java/Rust)
• Experience in both rapid prototyping and experimentation, as well as in bringing machine learning models into a stable and cost-effective production environment
• Business proficient in English (spoken and written)
• Experience in an advanced Engineering and Data Science role
• Extensive experience building machine learning and deep learning models on large datasets
• Fluent in Python and object-oriented programming
• Experience developing production software
• Experience in computer vision and recommendation systems

Qualifications: BS in Statistics with 8 years of Experience

• Significant experience in landing data science capability, applying the most effective statistical /machine learning models on real-world commercial problems and having measured the business benefits
• Strong statistical background applied across several areas, segmentations, NLP, predictive modelling, and recommendation systems
• Experience using both simple and sophisticated statistical models such as regression, clustering, affinity analysis, causal inference models, time series, convolutional neural networks, and transformers
• Comprehensive proficiency in key programming and scripting languages (e.g., Python, R, SPARK, SQL, etc) and software development skills
• Able to demonstrate innovation in approach and application
• Experience in building pricing models using machine learning techniques for motor insurance, risk cost models
• Experience in leading a small team of highly motivated data scientists
• Creative thinker and problem solver
• Ability to lead projects and drive them to completion in collaboration with other teams
• Proficient in using R, Python, etc.

Qualifications: BS in Applied Mathematics with 5 years of Experience

• Demonstrable experience in IS/IT with numerous years in a Data Science-related role
• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc
• Strong Expertise with Machine Learning and Artificial Intelligence technologies with coding experience in Python, Pandas, Databricks and experience with using the Azure Platform
• Experience in Reporting and Visualisation (Tableau, Power BI) to convey model output and insights
• Good understanding of Infrastructure (hardware, software, data, networking)
• Able to provide accurate estimates and meet deadlines
• Excellent interpersonal skills and strong abilities to develop and enhance relationships with stakeholders and peers
• Excellent written and verbal communication skills and ability to produce clear, concise, detailed documentation
• Flexible, pragmatic and professional approach

Qualifications: BS in Mathematics with 4 years of Experience

• Experienced in creating business models, both financial and consumer behavioral
• Extensive experience in statistical analysis, data mining and visualisation techniques
• Experience with data warehouse and MI environments and practices
• Strong understanding of data science methods using open source or commercial tools
• Experience in designing/architecting full Data Science stacks
• Experience in dealing with very granular data, using key Big Data technologies and comfortable with the Google environments
• Strong ability to present findings in a non-technical manner and to pitch at the right level
• High levels of logical thinking and problem-solving skills
• Able to deal with complex problems using innovative and industry-leading techniques
• Good understanding of Data Science swiss-army knife such as R, scikit-learn, Apache Spark, TensorFlow/MXnet, Stanford NLP/OpenNLP, STAN/BUGS, SAS, Python, Scala, Java
• Experience in managing and motivating a small team

Qualifications: BS in Artificial Intelligence with 6 years of Experience

• Knowledge of SQL and various statistical modeling or machine learning techniques
• Experience performing predictive analytics at a large-scale enterprise
• Experience in data science or advanced analytics in industry
• Must have SQL Experience
• Experience using multiple data systems and sources (such as Hadoop, Spark, Aster, Teradata, etc.)
• Working experience in building and deploying machine learning and predictive model solutions on large amounts of data
• Extensive hands-on experience in coding and modelling skills in Spark, Python, R, SQL, Presto, Hive proficiency
• Deep technical and data science expertise
• Experience building in-production models, including associated scripting, error handling and documentation
• Understanding of trade-offs between model performance and business needs
• Strong record of professional accomplishment
• Highly self-driven, demonstrate critical thinking, team player and fast learner
• Work experience and knowledge of more than one domain such as Risk Analytics, Marketing Analytics, Telecom analytics, Retail analytics, Fraud analytics, etc.

Qualifications: BS in Computer Engineering with 7 years of Experience

• Development experience in at least one programming language (like Python, R, Java, or C++)
• Industry experience solving analytical problems and building models using quantitative, statistical or machine learning approaches
• Experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis
• Experience with Machine Learning, Statistics, or other data analysis tools and techniques
• Experience performing data extraction, cleaning, analysis and presentation for medium to large datasets
• Experience writing SQL queries
• Experience with scientific computing and analysis packages such as NumPy, SciPy, Pandas, Scikit-learn, dplyr, or ggplot2
• Previous experience in managing a team
• Proficiency in algorithmic complexity 
• Ability to anticipate problems and work with incomplete information
• Highly passionate, self-motivated and driven individual who has a strong understanding of all aspects of Data Science and is happy to be hands-on working with the Team
• Strong desire to improve existing solutions and to innovate and introduce new and exciting ways to improve the current way that Data solutions are implemented at Christie’s
• Strong interpersonal skills, can demonstrate facilitation skills and the ability to set technical direction 
• Able to perform under pressure
• Understanding of business drivers
• Good problem-solving skills

Qualifications: BS in Data Engineering with 8 years of Experience

• Expert knowledge of machine learning
• Proficient with ML frameworks like TensorFlow, MXNet, sklearn, SageMaker, Torch or others
• Proficient with Python, Scala or Java
• Interest and ability to quickly learn and ramp up on new languages and technologies
• Knowledge of telecom networks
• Knowledge of big data concepts and technologies
• Experience with AWS, Spark, Flink, Kafka and NiFi ecosystems
• Experience with CI/CD and cloud deployments/infrastructure 
• Experience working in Agile R&D environments
• Experience in a data-science role

Qualifications: BS in Computational Science with 4 years of Experience

• Professional experience manipulating data sets, building statistical models, and productizing models along with experience applying Data Science methodologies to real-world problems
• Ability to drive projects to their completion
• Experienced in analyzing extremely complex, multi-dimensional data sets with a variety of tools
• Familiarity with at least one scripting language such as R, Python, or Scala
• Knowledgeable about data warehouse and non-relational data techniques to work with engineering partners
• Experience using SQL or other query languages
• Superior verbal, visual, and written communication skills to educate and work with cross-functional teams on controlled experiments
• Experience mentoring and coaching junior team members
• Experience working with stakeholders with non-data science backgrounds
• Programming experience in a system programming language such as C/C++ or Java 

Qualifications: BS in Bioinformatics with 5 years of Experience

• Experience from using statistics and machine learning to find insights from telemetry-type data mass
• Algorithm and signal processing expertise to support pod user presence algorithm development
• Ability to work together data/platform engineers to build data processing pipelines
• Expertise in integrating machine learning solutions into cloud and embedded systems
• Previous experience in Scrum and agile software development
• Experience from working in a remote, geographically dispersed team
• Good spoken and written language skills in English, Finnish skills
• Familiarity with working with IoT device data 
• Experience in Data Science
• Working experience in leading a team of Data Scientists
• Experience in developing models in Python with modern machine learning systems such as Scikit-learn, XGBoost, PyTorch or TensorFlow
• Expertise working with large data sets in distributed systems such as pyspark and Snowflake
• Experience in ML engineering, cloud-based deployment and model lifecycle management

Qualifications: BS in Quantitative Finance with 7 years of Experience

• Experience leading large data science projects, managing requirements across stakeholders to define the analytic approach and technical solutions
• Demonstrated ability to handle multiple projects, manage expectations, and deliver results in a fast-paced, dynamic environment
• Ability to scope, frame, and deliver data analyses that leverage large datasets including medical claims, pharmacy claims, and/or laboratory results
• Demonstrated understanding of advanced statistical methods used to estimate causal relationships in observational data
• Demonstrated ability to bring together and combine disparate datasets
• Demonstrated ability to develop nuanced study designs that are used to answer specific clinical questions
• Clear oral and written communication skills
• Experience interpreting and communicating results of complex analyses to technical and non-technical business partners and executive decision makers
• Professional experience leveraging large sets of structured and unstructured healthcare data
• Strong business acumen, including a deep understanding of healthcare payer economics

Qualifications: BS in Artificial Intelligence with 6 years of Experience

• Experience working in Data Science 
• Strong skills in hypothesis generation, data identification, statistics and testing principles
• Ability to turn insights into business use cases for innovation
• Strong written and verbal communication skills
• Moderate programming skills in SQL, R and/or Python
• Ability to aggregate, standardize, interpret and model data, and to work independently within a data warehouse or decentralized environment
• Keen eye for detail and for the thoughtful investigation of data before relying on it for analysis or modeling
• Basic understanding of modern machine learning and its mathematical underpinnings
• Ability to maintain focus on the business context and key decisions to be made
• Ability to manage multiple projects and deadlines simultaneously
• Basic knowledge of common statistical analysis methods (e.g., propensity, classification, text mining and sentiment analysis) and tools
• Familiarity with visualization methods and dashboard tools (e.g., Tableau)
• Experience partnering well with cross-functional teams, being highly collaborative, with the ability to build strong relationships
• Professional experience with Customer data
• Experience using the AWS big data technology stack
• Experience developing machine learning or advanced statistical models in any context

Qualifications: BS in Data Engineering with 8 years of Experience

• Strong mastery of statistics and ML algorithms and identifying 
• Proficiency in programming in languages such as Python, R, and SQL
• Experience working with cloud computing solutions
• Very good working knowledge of Azure Machine Learning
• Knowledge of frameworks, such as PyTorch, TensorFlow
• Knowledge of performing A/B test analyses to validate models
• Knowledge of code versioning systems, e.g., Git
• Knowledge of Docker, Big Data
• Independence and ability to coordinate work on analytical projects
• Ability to draw conclusions based on data and communicate with the business

Qualifications: BS in Industrial Engineering with 3 years of Experience

• Depth of knowledge in advanced analytics methods such as Support Vector Machines, Bayesian Inference, Markov Chain, Monte Carlo simulations, and more
• Breadth of knowledge across statistical methods, machine learning, and AI algorithms
• Ability to select the appropriate analytical techniques based on the characteristics of a problem
• Programming skills in languages like Python or R
• Familiarity with clean code and testing techniques
• Familiarity with large-scale data processing tools like Hadoop and Spark
• Strong algorithmic problem-solving skills
• Ability to design effective experiments in business and social environments
• Understand how to harvest complex data from a variety of sources
• Consultative skills and the ability to communicate complicated technical and analytical information to nontechnical audiences
• Knowledge and experience in software engineering, along with research experience

Qualifications: BA in Applied Economics with 5 years of Experience

• Experience designing, building, and evaluating practical machine learning solutions
• Experience with statistical programming language (e.g., Python, Spark, PySpark) and database languages (e.g., SQL)
• Excellent analytical skills, advanced level of statistics knowledge
• Strong expertise with Python and libraries such as scikit-learn, SciPy
• Familiarity with Bayesian modeling and probabilistic programming packages such as PyMC
• Familiarity with data platforms and applications such as Databricks, Jupyter, Snowflake, Airflow, and Github
• Familiarity with data exploration and data visualization tools such as Tableau, Looker
• Familiarity with designing and analyzing A/B testing and other experiment types
• Demonstrated skills in selecting the right statistical tools given a data analysis problem
• Ability to adapt quickly in a fast-moving environment with shifting priorities
• Strong communication skills for both technical and non-technical audiences
• Ability to handle multiple tasks concurrently and promptly, including large and complex ones
• Demonstrated leadership experience, including people and project management

Qualifications: BA in Economics with 6 years of Experience

• Experience in building and deploying Statistical and Machine Learning (ML) solutions using various supervised/unsupervised ML algorithms
• Working experience in coaching and technical leadership
• Working experience in the Financial Services or Automotive Industry
• Working experience in participating as a member of project teams using agile methodologies
• Data Governance / Management process application experience, i.e., Informatica EDC or equivalent training
• Working experience in ETL architecture and design 
• Expertise with data warehouse architecture and design experience, star schema, and Kimball-based solutions
• Experience using data to facilitate decisions 
• Substantial experience serving marketing or sales stakeholders, including proven impact from topics such as Attribution, Audience Segmentation, or Personalization
• Experience with statistical analyses using Python and relational database querying using SQL
• Working knowledge of standard machine learning algorithms and principles
• Strong communication skills, both written and verbal
• Experience in mentorship of data scientists (or other data professionals)
• Experience managing projects end-to-end in a cross-functional capacity
• Passionate about data science, analytics, and solving complex and sometimes ambiguous data problems

Qualifications: BS in Statistics with 8 years of Experience

• Experience with relational and non-relational databases
• Strong experience with tools for managing, analyzing, and visualizing large datasets
• Programming experience with two or more of R, Python, Lisp, Clojure, Scala, Java, Perl, Mathematica and C++
• Problem-solving versatility such as making the best decisions possible given the data quality and time available
• Strong analytical thinking and ability to quickly pick up new methods, tools and programming languages
• Experience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNet
• Experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, Jupyter notebooks, bash scripting, and Linux environment
• Prior experience coaching junior technical team members
• Strong background with planning and management of data science projects as well as leading engagement of stakeholders and communication with non-technical SMEs and partners
• Experience with the wireline telecommunications industry, notably common product technologies and marketing thereof

Qualifications: BS in Bioinformatics with 5 years of Experience