Job Summary: 

• Taking projects from the initial business case into detailed scoping and delivery of each analytics product
• Owning projects through delivery, ensuring time, cost/benefits, and quality of solutions are managed
• Establishing simple and effective governance with each product/business owner to ensure effective management of expectations and outcomes
• Leading a small team of data scientists and data engineers to design and build each analytics product
• Utilize internal solutions along with third-party vendor applications and where relevant, open source algorithms, continually scanning the market for innovative analytics and data science capabilities that could benefit business and clients
• Utilising the cloud analytics platform and ensuring future product needs are accommodated in the development roadmap
• Evangelize the use of Stock Modelling to promote the development
• Communicating with technical and non-technical stakeholders
• Define and drive analytic agenda, finding and prioritizing the most impactful opportunities to improve business and customer experience
• Working with team of data scientists to develop comprehensive analytic plans and execute analytic projects
• Work with cross-functional partners to establish new operating models, establish new solutions, and drive organizational change
• Find, screen, and work with external vendors who can provide data we do not have access to internally to improve analytic capabilities

Skills on Resume: 

• Project Management (Hard Skills)
• Team Leadership (Soft Skills)
• Stakeholder Communication (Soft Skills)
• Data Analytics (Hard Skills)
• Vendor Management (Hard Skills)
• Cloud Computing (Hard Skills)
• Change Management (Soft Skills)
• Market Research (Hard Skills)

Job Summary: 

• Understand product, risk, and business requirements and how to apply ML to solve the most challenging problems in impactful ways
• Creatively leverage new and existing data to increase the effectiveness and efficiency of decision-making infrastructure
• Partner with ML engineers to design machine learning solutions that operate quickly and effectively at scale
• Make business recommendations to the executive and cross-functional teams (e.g. cost-benefit, forecasting, experiment analysis) effectively through presentations of findings and visualizations of quantitative information
• Lead efforts to build the next generation of data products at Chime
• Feature Engineering to prepare data sets as available from acquisition pipeline, ontology and graph to build robust models
• Build a differentially private federated learning system for all current and future ML models to ensure training data is protected and remains local while models are globally available
• Ensure models are explainable and unbiased using open-source libraries while looking at opportunities to augment them to fit needs
• Use expertise to advise delivery teams engaged in projects with big pharma.
• Developing advanced machine learning algorithms and statistical models to solve critical problems and help deliver the best customer experiences. 
• Partners with Business teams, the Platform team, the Security team, and other stakeholders to implement data science models into live production systems. 
• Brings fresh perspectives to inform decision-making toward better player experience by translating player voice into insights.

Skills on Resume: 

• Machine Learning (Hard Skills)
• Data Visualization (Hard Skills)
• Feature Engineering (Hard Skills)
• Statistical Modeling (Hard Skills)
• Cross-Functional Collaboration (Soft Skills)
• Data Privacy (Hard Skills)
• Business Analysis (Hard Skills)
• Problem Solving (Soft Skills)

Job Summary: 

• Using Python and Postgresql (and on occasion Excel) for user-level data analysis
• Working with data warehouse, specifically through MODE and HEAP analytics
• Working within the Data Team and alongside development teams to better scale data collection of relevant product and user data points
• Assisting in building necessary data infrastructure to continue to expand analysis capabilities
• Analyzes data, as well as contributes to the design, implementation, and delivery of analytics products and services.
• Builds predictive models, tools, and data visualizations.
• Oversees and/or leads research development projects and data science project plans for clients and internal initiatives.
• Drive the selection of innovative technologies to solve problems that nobody yet could solve. 
• Develop machine learning models to structure and analyze scientific data
• Use NLP and statistical models to classify data
• Collaborate with data quality teams to define training sets and metrics
• Verify that the outcomes meet the acceptance criteria

Skills on Resume: 

• Python Programming (Hard Skills)
• Data Analysis (Hard Skills)
• Predictive Modeling (Hard Skills)
• Data Visualization (Hard Skills)
• Machine Learning (Hard Skills)
• Natural Language Processing (Hard Skills)
• Collaboration (Soft Skills)
• Research Development (Soft Skills)

Job Summary: 

• Understanding business challenges and creating valuable actionable insights
• Perform 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
• Consider how the models will work at the terminal scale
• Collaborate with engineering teams to implement models
• Lead and take full responsibility over an initially small team, with a vision for team growth
• Envision, create, and execute a Data Science roadmap
• Collaborate with business, analytics, product, and engineering teams to gather requirements, and design data science models in various arenas 
• Lead the process to build, test, train, deploy and iterate production quality machine learning model artifacts that run in batch as well as real-time
• Work with data engineers to build solid data pipeline input into data science models
• Develop an expert-level understanding of business models and operations, and innovate to advance business through machine learning

Skills on Resume: 

• Predictive Analytics (Hard Skills)
• Data Cleaning (Hard Skills)
• Model Implementation (Hard Skills)
• Team Leadership (Soft Skills)
• Data Science Roadmap (Hard Skills)
• Collaboration (Soft Skills)
• Machine Learning (Hard Skills)
• Data Pipeline Development (Hard Skills)

Job Summary: 

• Collaborate with AO leaders, staff & 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
• Design, develop and pilot/ deploy state-of-the-art, data-driven optimization algorithms, forecasting and predictive/descriptive models beyond current PHS analytics capabilities to 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 & 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
• 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

Skills on Resume: 

• Requirement Analysis (Soft Skills)
• Statistical Modeling (Hard Skills)
• Risk Mitigation (Soft Skills)
• Data Visualization (Hard Skills)
• Predictive Analytics (Hard Skills)
• Advanced-Data Analysis (Hard Skills)
• Machine Learning (Hard Skills)
• Exploratory Data Analysis (Hard Skills)

Job Summary: 

• Work with the Head of the Department to scope out a vision for the Data Science practice within this multi-national organization.
• Liaise with internal stakeholders across the business to propose a PoC for the function.
• Build toward managing a team of Data Scientists.
• Application of Deep Learning for specific use around Customer Science.
• Manage and collaborate with another analytics stakeholder to define and pilot prototype BI tools
• Design and develop reusable analytical assets (data structures, code, solutions), recommend improvements to existing assets, and evaluate 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 work product of peers & 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 PHSs goals.

Skills on Resume: 

• Team Management (Soft Skills)
• Deep Learning Application (Hard Skills)
• Stakeholder Engagement (Soft Skills)
• Prototype Development (Hard Skills)
• Analytical Asset Development (Hard Skills)
• Talent Training (Soft Skills)
• Quality Assurance (Hard Skills)
• Thought Leadership (Soft Skills)

Job Summary: 

• Manage relationships with external data science partners
• Improve the quality and integrity of the data
• Drive operational efficiency through increased automation of data capture
• Explore and introduce new data sets
• Build analytic systems and predictive models
• Test performance of data-driven products
• Visualize data and create reports
• Identify proper data sources necessary for projects and ensure they are accurately imported and joined. 
• Create an execution plan and assist data scientists in their efforts to implement.
• Identify proper analytic methodology and ensure analytic efforts are executed correctly. 
• Create execution plans and assist data scientists in efforts to implement.
• Ensure all projects have proper documentation taking into account potential regulatory, legal, or business concerns

Skills on Resume: 

• Relationship Management (Soft Skills)
• Data Quality Improvement (Hard Skills)
• Operational Efficiency (Hard Skills)
• Predictive Modeling (Hard Skills)
• Data Visualization (Hard Skills)
• Project Documentation (Hard Skills)
• Methodology Identification (Hard Skills)
• Execution Planning (Hard Skills)

Job Summary: 

• Provide direction, training and growth opportunities to data scientists on the team
• Collaborate with highly experienced engineers, data scientists, data engineers, and domain experts to develop well-defined problem statements for high-impact problems
• Develop sophisticated algorithms, automated processes, and software using large datasets from multiple disparate sources
• Collaborate with peers globally to establish Best Known Methods (BKMs) for standards, efficiency, and deployment
• Use in-depth data science expertise to influence the data science lifecycle
• Develop and deploy end-to-end data science solutions from the idea generation phase, PoC development to full productization.
• Drive actions and business impact through effective data presentation and data storytelling
• Fully lead data scientists functionally, including division of tasks/resources across projects and ensuring data scientists are productive and grow.
• Lead communication of project results/challenges to business partners in ways they can understand. 
• Proactive communication of delays to planned efforts.
• Ensure project tasks are fully planned, including understanding how implementation affects other units, how to measure results, how to achieve the expected benefit, etc.
• Identify and propose new technologies and tools that could benefit the Data & Analytics team.

Skills on Resume: 

• Team Development (Soft Skills)
• Algorithm Development (Hard Skills)
• Collaboration (Soft Skills)
• Data Science Expertise (Hard Skills)
• Data Storytelling (Soft Skills)
• Project Management (Hard Skills)
• Communication Skills (Soft Skills)
• Technology Innovation (Hard Skills)

Job Summary: 

• Lead a team of data scientists, offering coaching and development guidance
• Engage with key partners across different levels of the business to drive value outcomes from data science projects.
• Work with Subject Matter Experts and other business partners in Finance and Technology, to translate business problems into data science problems and to devise solutions, with measurement frameworks for key KPIs.
• Implement a highly visual and commercial approach when delivering data science projects that engage and challenge the thinking of non-technical audiences.
• Build production data science products by promoting robust software development standards and practices within teams, including testing and code reviews.
• Help drive the development of a high-functioning organization through the deployment of cutting-edge computational techniques applied to a wide range of data, including structured and unstructured healthcare data sources, patient-generated data, and complementary real-world information streams.
• Manage end-to-end data science projects, including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
• Provide operational and scientific support on multiple projects with a core role in delivering expertise in machine learning, modeling, simulation, and NLP/text mining on real-world data across Sanofi.
• Work with developers, engineers, and MLOps to deliver robust and scalable machine learning solutions.

Skills on Resume: 

• Team Leadership (Soft Skills)
• Stakeholder Engagement (Soft Skills)
• Data Translation (Hard Skills)
• Project Management (Hard Skills)
• Software Development Standards (Hard Skills)
• Machine Learning Expertise (Hard Skills)
• Communication Skills (Soft Skills)
• Data Science Architecture (Hard Skills)

Job Summary: 

• Identify the proper data sources necessary for projects and ensure they are accurately imported and joined
• Create an execution plan and assist data scientists in their efforts to implement
• Identify proper analytic methodology and ensure analytic efforts are executed correctly
• Create execution plans and assist data scientists in efforts to implement
• Ensure all projects have proper documentation, taking into account potential regulatory, legal, or business concerns
• Lead communication of project results/challenges to business partners in ways they can understand
• Proactive communication of delays to planned efforts
• Ensure project tasks are fully planned, including understanding of how implementation affects other units, how to measure results, how to achieve the expected benefit, etc.
• Identify and propose new technologies and tools that could benefit the Data and Analytics team

Skills on Resume: 

• Data Sourcing (Hard Skills)
• Plan Execution (Hard Skills)
• Method Selection (Hard Skills)
• Project Documentation (Hard Skills)
• Result Communication (Soft Skills)
• Delay Reporting (Soft Skills)
• Task Planning (Hard Skills)
• Tech Exploration (Hard Skills)

Job Summary: 

• Builds, implements, and maintains predictive models for business problems with no current solution
• Works in both independent and collaborative team environments
• Drives continuous improvement of existing models
• Engages with cross-functional teams including IT, operations, data engineers, ML engineers, and external partners
• Develops Python code in enterprise on-prem or AWS or Azure cloud environments
• Interprets, communicates, and presents analytic results to business executives
• Builds or supports the building of business cases related to data science projects
• Guides the application of data science within other departments and the use of external vendors
• Assists the Principal Data Scientists in the development of data science standards for the department
• Reviews code and modeling approaches of other Data Scientists
• Mentors and assists in technically supervising less experienced Data Scientists

Skills on Resume: 

• Model Development (Hard Skills)
• Team Collaboration (Soft Skills)
• Model Improvement (Hard Skills)
• Cross-Functional Engagement (Soft Skills)
• Python Programming (Hard Skills)
• Result Presentation (Soft Skills)
• Business Casework (Hard Skills)
• Technical Mentoring (Soft Skills)

Job Summary: 

• Work with Product Managers and the Marketing teams to scope new Data Science products to support growth
• Work together with the Software Engineering team to productionise and integrate ML algorithms into backend systems
• Lead, coach, and grow a team of accomplished data scientists and engineers to achieve value
• Work closely with the Data team to understand the value of customer segments and devise a strategy for how to effectively target individual segments
• Develop ML algorithms focusing in particular on Marketing channels optimisation and spend efficiency
• Stay up to date with industry innovations, latest martech and best practices to continually improve and bring best-in-class thinking to On the Beach
• Identify the triggers for the highest LTV customers and ways to improve customer loyalty
• Transform abandoned basket strategy to deliver a best-in-class programme of activity to convert users by better understanding barriers and the user journey (pretty complex in holiday purchase)
• Overhaul approach to capturing data to give richer data from more users
• Identify innovative new ways of understanding the dynamics between marketing channels

Skills on Resume: 

• Product Scoping (Hard Skills)
• Model Integration (Hard Skills)
• Team Leadership (Soft Skills)
• Segment Strategy (Hard Skills)
• Algorithm Development (Hard Skills)
• Industry Awareness (Soft Skills)
• Customer Insight (Hard Skills)
• Data Enrichment (Hard Skills)

Job Summary: 

• Working with the IDS team and R&D teams (Hsinchu and Global teams) to efficiently and effectively support and implement scientific High Performance Computing and data science project systems
• Provide direction to the IDS High Performance Computing and data science teams (Hsinchu and Global teams), supporting vendor and contract support organization
• Lead personnel/career development processes for the IDS High Performance and data science team
• Develop budget recommendations on capital investment and operating costs (5-year plans)
• Develop annual and long-term operating budgets
• Operate the team within these guidelines
• Conduct monthly analysis to ensure costs comply with budgetary goals and functional guidelines
• Develop/implement resource and infrastructure requirements to meet short and long-term R&D needs (investment, personnel, etc) in partnership with the DuPont IT organization
• Sponsor and drive strong quality management practices
• Drive continuous improvement projects and foster a focused-improvement culture in all employees and operations
• Collaborate with the data science community to identify leading-edge technology for implementation to advance DuPont's data science capabilities
• Communicate effectively to provide timely updates to R&D business and regional leadership
• Facilitate employee development, coaching, counseling, mentoring, motivation, and policy-setting to create an environment for employees and work teams that support commitment to continuous improvement and empowerment
• Conducts annual and long-term succession planning

Skills on Resume: 

• HPC Coordination (Hard Skills)
• Team Direction (Soft Skills)
• Career Development (Soft Skills)
• Budget Planning (Hard Skills)
• Cost Analysis (Hard Skills)
• Resource Planning (Hard Skills)
• Quality Management (Hard Skills)
• Tech Collaboration (Soft Skills)

Job Summary: 

• Plan and lead data science/machine learning projects within the team
• Lead and mentor teams focused on developing production-grade services and capabilities
• Design and implement machine learning models for several financial applications including Transaction Classification, Temporal Analysis, and Risk modeling using structured and unstructured data
• Measure, validate, implement, monitor and improve the performance of machine learning models
• Analyze massive amounts of data to provide insights
• Work with product leaders and clients to enable market-facing solutions using data science
• Present findings to internal business leaders as well as to clients
• Leverage best practices in machine learning and data engineering to develop scalable solutions
• Assist with talent acquisition efforts and facilitate training programs within the team
• Propose creative solutions to existing challenges that are new to the company, the financial industry and to data science

Skills on Resume: 

• Project Leadership (Soft Skills)
• Team Mentoring (Soft Skills)
• Model Design (Hard Skills)
• Model Evaluation (Hard Skills)
• Data Analysis (Hard Skills)
• Client Collaboration (Soft Skills)
• Insight Presentation (Soft Skills)
• Scalable Engineering (Hard Skills)

Job Summary: 

• Process, filter, and automate large amounts of text data pipelines to uncover actionable business insights
• Help create the vision for future analytical studies that use advanced analytics technologies
• Identifies opportunities where analytics can be used to identify and measure business results
• Use innovative technologies such as machine learning, predictive analytics and textual analysis to build models
• Leverage Deep learning / AI / NLP and develop a text analytics solution to extract information
• Build deep partnership with business and technology stakeholders from across the enterprise to understand and existing available data sources
• Understand and contribute to data science best practices at ADP
• Conduct statistical analysis, build models, and surface insights that enable the client-facing organization to make better decisions
• Develop models and analyze interaction and transaction data to identify patterns, uncover opportunities, and create executable analytics to drive revenue
• Identify and use appropriate investigative and analytical technologies to interpret and verify results
• Develop statistical and machine learning techniques to build models that improve how to engage with clients
• Apply and learn a wide variety of tools and languages to achieve results (e.g., Python, R, SPSS, Hadoop)
• Design scalable data pipelines and data integrations to collect, clean and store datasets

Skills on Resume: 

• Text Processing (Hard Skills)
• Analytics Vision (Soft Skills)
• Opportunity Identification (Soft Skills)
• Model Building (Hard Skills)
• NLP Development (Hard Skills)
• Stakeholder Partnership (Soft Skills)
• Statistical Analysis (Hard Skills)
• Pipeline Design (Hard Skills)

Job Summary: 

• Designing and deploying Machine Learning algorithms to solve key problems such as anomaly detection for system reliability, predictive maintenance, process optimization, domain-specific language models, and production optimization 
• Collaborating with data and subject matter experts from BHC3 and its customers to seek, understand, validate, and interpret new data elements
• Setting up and promoting rigorous processes for code review, data quality assessment and engineering reviews
• Presenting project plans, technical roadmaps, risks and recommendations to senior business leaders within the technical space and occasionally to senior leaders in partner technical teams
• Tracking, sharing and disseminating state-of-the-art in ML/AI and industrial analytics with the BHC3 data science team and with the broader data science community
• Help drive the experimentation of new and emerging technologies, analytical capabilities, and data models to understand their potential and value for the organisation
• Particularly with new tech and data
• Collaborate to build peer and senior relationships across the organisation and the Innovation eco-system including across government, academia, business and start-ups share
• Work with and challenge senior stakeholders

Skills on Resume: 

• Algorithm Design (Hard Skills)
• Data Collaboration (Soft Skills)
• Process Rigor (Hard Skills)
• Technical Presentation (Soft Skills)
• ML Research (Hard Skills)
• Tech Experimentation (Hard Skills)
• Relationship Building (Soft Skills)
• Stakeholder Management (Soft Skills)

Job Summary: 

• Own new and existing initiatives around advertisement predictive modeling
• Lead the life cycle of Machine Learning including data transformation, exploratory data analysis, feature engineering, model trial pipelines, measuring performance, retraining, and deploying/ productizing models
• Act as a thought leader across multiple business functions to formulate DS-centric opportunities that lead to defined results
• Manage and mentor a growing team of data scientists
• Identifying, exploring and transforming data for the General Insurance Team
• Deliver machine learning systems to ensure that new insights are turned into real business value
• Use research skills to creatively tackle the most impactful business problems
• Support, train and collaborate with the wider data science team and non-technical business partners
• Understanding business problems and how they can be solved through researching, designing and building models

Skills on Resume: 

• Model Ownership (Hard Skills)
• ML Lifecycle (Hard Skills)
• Thought Leadership (Soft Skills)
• Team Management (Soft Skills)
• Data Exploration (Hard Skills)
• System Delivery (Hard Skills)
• Research Creativity (Soft Skills)
• Business Modeling (Hard Skills)

Job Summary: 

• Research opportunities to use data across the product surface area
• Design, test, build and deploy fit-for-purpose ML models
• Monitor and maintain ML models that are in production
• Craft and deliver well-designed software required to serve ML models
• Analyse, create, test and deploy data and feature sets built from a mix of raw and modelled data using Airtasker’s data engineering pipeline
• Contribute to product roadmaps through data exploration and analysis
• Ensure quality experimentation by guiding experiment design, monitoring and the interpretation of results
• Educate the organisation on the data product lifecycle
• Supervise and mentor junior team members

Skills on Resume: 

• Model Design (Hard Skills)
• Model Monitoring (Hard Skills)
• Software Development (Hard Skills)
• Feature Engineering (Hard Skills)
• Data Exploration (Hard Skills)
• Experiment Guidance (Soft Skills)
• Team Education (Soft Skills)
• Staff Mentoring (Soft Skills)

Job Summary: 

• Working with internal interlocutors within the organization to define requirements and identify opportunities for using data to guide business solutions
• Analyze data from different sources to support the optimization of product development, marketing, strategy and activation activities
• Validate the efficiency and accuracy of sources and data acquisition techniques
• Develop and implement classic ML models and statistical models to obtain actionable business insights
• Use descriptive and predictive models to improve the customer experience, revenue generation and advertising targeting
• Apply test methodologies (A/B and multivariate) and model quality validation
• Liaise with the Engineering / IT team to implement new Data Pipelines
• Assess the effectiveness of data sources and data-gathering techniques and improve data collection methods
• Identify opportunities to use insights/datasets/code/models across other functions in the organisation (mainly in the e-commerce/CRM, Finance and Marketing areas)
• Being able to stay up to date with the latest technology, techniques and methods
• Lead external technology partners to scale the analytics project for success

Skills on Resume: 

• Requirement Definition (Soft Skills)
• Data Analysis (Hard Skills)
• Data Validation (Hard Skills)
• Model Development (Hard Skills)
• Predictive Modeling (Hard Skills)
• Test Methodology (Hard Skills)
• Pipeline Implementation (Hard Skills)
• Insight Sharing (Soft Skills)

Job Summary: 

• Critically reviewing the appropriateness of ML models with respect to the modeling objectives and the available development data
• Evaluating the developmental testing approach and results for individual models
• Produce high-value model validation reports, including highlighting risks and limitations of the model
• Assess the ongoing performance monitoring of models post-deployment
• Contribute to regulatory and internal audit-related responses
• Contribute to regulatory drafting sessions with the insurance trade association on behalf of the Company
• Research-driven mindset, with the ability to lead the process of designing, developing, delivering and maintaining model testing best practices, standards, and templates
• Follow a strict code of ethics and protect sensitive information at all times
• Collaborate with product and business teams to understand all aspects of the problem
• Define the right target metrics that best represent the end-user value
• Apply knowledge of ML, statistics, and advanced mathematics to conceptualize, experiment and design an intelligent system
• Build efficient systems for processing large amounts of data
• Work closely with ML Engineers to come up with scalable system and model architectures for enabling real-time ML/AI services
• Build and own ML pipelines end-to-end, including stages such as data pre-processing, model generation, cross-validation, and share feedback

Skills on Resume: 

• Model Evaluation (Hard Skills)
• Validation Reporting (Hard Skills)
• Performance Monitoring (Hard Skills)
• Regulatory Support (Soft Skills)
• Research Leadership (Soft Skills)
• Metric Definition (Hard Skills)
• System Design (Hard Skills)
• Pipeline Management (Hard Skills)

Job Summary: 

• Build industry-leading credit, fraud, and pricing models to drive real-time lending decisions for hundreds of thousands of borrowers every month
• Leverage advanced machine learning techniques and complex data sources such as credit bureau information and alternative data sources, at a large scale to build sophisticated credit and fraud models
• Collaborate with product and engineering teams to deploy models into the production environment
• Evaluate new statistical and modeling approaches and alternative data sources to drive better credit decisions
• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions
• Optimize complex SQL programs for speed and efficiency
• Contribute to the design, development, and implementation of Sanofi’s data science architecture and ecosystem to guide decision-making and building of foundational capabilities
• Maintain internal and external profile through contributions to congresses and publications

Skills on Resume: 

• Model Building (Hard Skills)
• Machine Learning (Hard Skills)
• Model Deployment (Hard Skills)
• Approach Evaluation (Hard Skills)
• Data Opportunity (Soft Skills)
• SQL Optimization (Hard Skills)
• Architecture Contribution (Hard Skills)
• Scientific Engagement (Soft Skills)

Job Summary: 

• Apply statistical analysis, machine learning techniques, predictive modeling, and data mining to solve business problems in credit, fraud, and identity theft
• Lead and execute complex modeling/machine learning projects and new product development from concept to final delivery including writing code for model deployment
• Work cross-functionally with various internal departments and external clients
• Innovate, propose, and design new revenue-generating products
• Develop analytic solutions using machine learning techniques including Predictive Modeling, Text Mining, Entity Resolution, Time-series Forecasting, Network Analytics, Computer Vision, and Automated Speech Recognition
• Ensure that solutions meet specified technical requirements promptly
• Lead and deliver data science projects from start to finish, working closely with stakeholders to ensure a measurable business impact
• Guide and coach junior team members to help them develop their data science skills
• Identify improvement opportunities across the data science landscape, proactively advising the team on this and taking action to prevent and fix critical problems across ML solutions with the wider team
• Partner with stakeholders on data and technical issues, guiding technical implementation feasibility

Skills on Resume: 

• Modeling Expertise (Hard Skills)
• Project Leadership (Soft Skills)
• Cross-Functional Collaboration (Soft Skills)
• Product Innovation (Hard Skills)
• Machine Learning (Hard Skills)
• Solution Delivery (Hard Skills)
• Team Coaching (Soft Skills)
• Issue Resolution (Hard Skills)

Job Summary: 

• Collaborate with clients, partners and colleagues to understand people-related business problems and gather relevant people data to analyse
• Utilise existing data, complete research and develop primary data collection mechanisms 
• Build statistical models that explain or predict outcomes related to hiring, productivity, engagement, and attrition
• Scale data analysis or insights using AI/ML techniques, best-in-class data engineering principles, and modern data standards (e.g., APIs)
• Prepare and deliver presentations with rich data visualizations and meaningful business insights and conclusions
• Develop data science skills as new or improved techniques arise, and continue exploration of emerging methods and technologies in the analytics space
• Build trusted, strategic relationships with HR, Procurement and Business executives within client organisations
• Coach colleagues and client stakeholders on how best to understand and use workforce data to shape talent strategies
• Working closely with Fortune 500, FTSE 100 or CAC40 clients across a broad range of industries including Financial Services, FMCG, Life Sciences and Technology

Skills on Resume: 

• Client Collaboration (Soft Skills)
• Data Collection (Hard Skills)
• Statistical Modeling (Hard Skills)
• AI Scaling (Hard Skills)
• Insight Presentation (Soft Skills)
• Development (Soft Skills)
• Stakeholder Relations (Soft Skills)
• Data Coaching (Soft Skills)

Job Summary: 

• Solve business problems by leveraging techniques such as segmentation, optimization, advanced analytics and machine learning
• Create reports and dashboards to closely monitor performance metrics and provide insights
• Automate datasets, production process and reporting to solve for analytical needs
• Create test plans to drive growth in applicants and customers
• Create a campaign performance measurement framework
• Execute plans to drive deeper engagement, loyalty and lifetime value
• Collaborate with web and digital marketing peers to enable cross-channel campaigns
• Maintain ROI goals, CAC and other performance benchmarks

Skills on Resume: 

• Analytics Modeling (Hard Skills)
• Performance Reporting (Hard Skills)
• Process Automation (Hard Skills)
• Test Planning (Hard Skills)
• Campaign Measurement (Hard Skills)
• Customer Engagement (Soft Skills)
• Marketing Collaboration (Soft Skills)
• ROI Management (Hard Skills)

Job Summary: 

• Develop and maintain new and existing modelled data pipelines 
• Ensure their ‘Source of Truth’ datasets are stable, accurate and reliable 
• Take a lead on overall team code quality with emphasis on SQL and Python 
• Design and implement testing frameworks to monitor data quality 
• Assist with best practices for development and deployment - packaging, CI / CD, Gitflow 
• Drive continuous performance improvements and efficiency savings within their Snowflake data warehouse and wider analytical tooling 
• Engage in design sessions to help build new data products for the business 
• Deliver projects in an agile, scrum-based environment 

Skills on Resume: 

• Pipeline Development (Hard Skills)
• Data Reliability (Hard Skills)
• Code Quality (Hard Skills)
• Quality Testing (Hard Skills)
• Deployment Practices (Hard Skills)
• Performance Improvement (Hard Skills)
• Product Design (Soft Skills)
• Agile Delivery (Soft Skills)

Job Summary: 

• Join the research and development of new innovative capabilities for cybersecurity, involving advanced AI and Algorithms
• Quickly iterate on design approaches and POCs based on data-driven research and user feedback
• Design and develop novel data-driven solutions that have the potential to deliver game-changing results involving advanced AI and Algorithms
• Push the solutions to a large-scale production system
• Understand the architectural constraints of such systems and work with a cross-organizational engineering and product team to quickly transition from prototype to a scalable, robust implementation
• Communicate and align with the stakeholders about requirements
• Develop algorithms and models
• Iterate and roll out models into production and perform the necessary analysis
• Document and present the technologies developed

Skills on Resume: 

• AI Research (Hard Skills)
• Rapid Prototyping (Hard Skills)
• Solution Design (Hard Skills)
• Model Deployment (Hard Skills)
• System Integration (Hard Skills)
• Stakeholder Communication (Soft Skills)
• Algorithm Development (Hard Skills)
• Technical Documentation (Hard Skills)

Job Summary: 

• Lead the development of new and innovative data science products
• Understand business challenges and client requirements
• Conduct research into and propose solutions, new strategies, and/or new products
• Lead data acquisition, data analytics, as well as the prototyping, building, and training of machine learning models
• Lead the deployment of models to web services and data centers
• Monitor model performance, scoring and re-training
• Interact with team members, management, shareholders, and clients
• Provide mentoring and training to Data Scientists and Senior Data Scientists
• Synchronize the Data Science team with the Security Research team to achieve business value

Skills on Resume: 

• Product Development (Hard Skills)
• Requirement Analysis (Soft Skills)
• Solution Research (Hard Skills)
• Model Building (Hard Skills)
• Model Deployment (Hard Skills)
• Performance Monitoring (Hard Skills)
• Stakeholder Interaction (Soft Skills)
• Team Mentoring (Soft Skills)

Job Summary: 

• Taking ownership or enabling effective design of solutions to problems, old and new
• Performing or enabling the effective deployment of solutions and models into production
• Working in a cloud environment, with containerised pipelines (Docker, Kubernetes)
• Developing using Git and CI/CD processes
• Building and maintaining a close working relationship with other internal teams such as data engineering, DevOps, application engineering and product owners
• Building a robust, no single-point-of-failure team to ensure successful project delivery
• Working closely with relevant product managers on managing expectations, communications, understanding requirements, building/delegating developments, monitoring backlogs and deciding priorities, most likely every week
• Working in and supporting the growth of an Agile framework within teams and projects
• Building up the knowledge and experience in the audience measurement space
• Working closely with other teams in the area (other Data Science teams, Data Engineering and Data Ops teams)
• Helping champion and educate wider Ipsos on the capabilities that data science can bring
• Creating an effective upskilling plan for juniors

Skills on Resume: 

• Solution Design (Hard Skills)
• Model Deployment (Hard Skills)
• Cloud Engineering (Hard Skills)
• CI Development (Hard Skills)
• Team Collaboration (Soft Skills)
• Team Building (Soft Skills)
• Expectation Management (Soft Skills)
• Agile Support (Soft Skills)

Job Summary: 

• Designs, develops, solves, and programs models, methods, processes, and systems to analyze unstructured/structured datasets to generate actionable insights and solutions for internal and external partners
• Interacts with product, sales, and customer service teams to identify questions and issues for data analysis and experiments
• Develops and codes software programs, models, algorithms, and automated processes to cleanse, integrate and evaluate datasets from multiple sources
• Develops and evaluates advanced algorithms, including descriptive, measurement, predictive, optimization and prescriptive models that lead to tactical and strategic business insights
• Applies knowledge of statistics, econometrics, operations research, machine learning, deep learning methods, computer programming, data engineering, simulation techniques, and advanced mathematics at the MS and Ph.D. level to model market behavior, uncover patterns in large datasets
• Identify market opportunities, pose meaningful business questions
• Make valuable discoveries that lead to prototype development, full-scale product deployment, and improvements with existing products
• Helps usher large-scale product implementations from inception to post-rollout support
• Manages large projects or processes
• Coaches, reviews and delegates work to lower-level professionals
• Problems faced are difficult and often complex

Skills on Resume: 

• Model Development (Hard Skills)
• Cross-Functional Interaction (Soft Skills)
• Data Engineering (Hard Skills)
• Algorithm Evaluation (Hard Skills)
• Statistical Modeling (Hard Skills)
• Opportunity Identification (Soft Skills)
• Product Deployment (Hard Skills)
• Team Coaching (Soft Skills)

Job Summary: 

• Supervise, monitor and work with data team members to execute work activities to meet project deliverables as expected and promptly
• Work as a liaison to the Senior Director of Technology to understand and align data science team capacity, skills, availability and expertise to organizational needs, projects and work activities
• Promote teamwork across departments by sharing knowledge, providing mentorship and training for staff, and cooperating with others
• Participating in meetings and work groups, and supporting the goals and objectives of the organization
• Create and refine processes and documentation for the organization’s data science department
• Provide subject matter expertise and oversight to a team of data scientists and data analysts in the collection, preparation, analysis, management, integration and visualization of longitudinal data sets from a variety of sources and industry sectors
• Create innovative solutions to complex, unique problems transcending multiple areas of focus and expertise, including education, healthcare, public service, workforce and related domains
• Hands-on contributor to projects that require data manipulation and coding in a platform-agnostic environment
• Develop, apply, and interpret complex machine learning and statistical models that evaluate impact on life outcomes, program effectiveness, systemic coherence and efficacy, and existing practices
• Brings a mindset of continuous improvement, growth and constant learning to embrace and institutionalize new approaches, emergent trends and evolving practices

Skills on Resume: 

• Team Supervision (Soft Skills)
• Capacity Alignment (Soft Skills)
• Cross-Department Collaboration (Soft Skills)
• Goal Support (Soft Skills)
• Process Documentation (Hard Skills)
• Data Oversight (Hard Skills)
• Solution Innovation (Hard Skills)
• Data Modeling (Hard Skills)

Job Summary: 

• Lead the development and implementation of data science solutions from beginning to end
• Translate business needs into data science problem statements to develop predictive analytics models and applications
• Explore both internal and external data sources to proactively identify opportunities for innovation to deliver data-centric business insights that support business decisions
• Manage the delivery of PoC projects and work with the Reporting and Analytics Lead to formulate the business case for any new and innovative products and applications
• Facilitate resolution of complexities and simplification of solution options by bringing together business and technical SMEs, i.e., data scientists, front-end and back-end developers, and other relevant resources
• Present insights to PwC leaders and C-suite executives regularly using business acumen, as well as an ability to translate complex technical concepts into business terms and actionables
• Manage the transition of data science projects into business-as-usual self-service applications
• Work with the Reporting and Analytics Lead to formulate the product roadmap and the at-scale implementation strategy

Skills on Resume: 

• Solution Leadership (Hard Skills)
• Problem Translation (Soft Skills)
• Data Exploration (Hard Skills)
• PoC Management (Soft Skills)
• SME Facilitation (Soft Skills)
• Insight Presentation (Soft Skills)
• Project Transition (Hard Skills)
• Roadmap Planning (Soft Skills)

Job Summary: 

• Manage the service demands of predictive analytics products and liaise with business stakeholders to verify insight needs and eliminate ambiguity
• Manage stakeholder expectations by providing achievable timelines for outputs and ensuring stakeholder satisfaction
• Actively solicit stakeholder feedback for improving the data science services
• Propose appropriate recommendations for service, process and application improvements and optimization needs based on user/stakeholder feedback
• Promote the use of self-serve analytics and insights delivered via the Shiny apps and RStudio suite of products
• Drive, deliver and maintain the data science standards/principles, advocate the best practice use of the self-service capabilities to ensure the consistency of delivered insights
• Liaise with the relevant service teams in IT, Global Chief Data Office, NIS and Finance to support development activities, data quality and availability 
• Assure the quality, accuracy and security of outputs before sign-off and distribution

Skills on Resume: 

• Service Management (Soft Skills)
• Expectation Management (Soft Skills)
• Feedback Solicitation (Soft Skills)
• Process Improvement (Hard Skills)
• Self-Serve Promotion (Soft Skills)
• Standards Advocacy (Hard Skills)
• Team Liaison (Soft Skills)
• Output Assurance (Hard Skills)

Job Summary: 

• Join a global, international team in a flexible working environment
• Evaluate current methodologies and identify opportunities for enhancement (input data cleaning, data preparation methodologies, data quality tracking, consumer projection)
• Present and communicate findings and recommendations based on research and analysis
• Design end-to-end validation/projection performance indicators in connection with the product quality
• Develop the prototypes of new solutions/methodologies to new market challenges
• Support production deployment of proposed enhancements/new solutions
• Support documentation of findings, methodologies, and best practices
• Take requirements for analyses, reports
• Build documentation and playbooks for space
• Manage stakeholder relationships and related forums
• Deliver projects and assets iteratively

Skills on Resume: 

• Methodology Evaluation (Hard Skills)
• Insight Presentation (Soft Skills)
• Performance Design (Hard Skills)
• Prototype Development (Hard Skills)
• Solution Deployment (Hard Skills)
• Process Documentation (Hard Skills)
• Stakeholder Management (Soft Skills)
• Iterative Delivery (Soft Skills)

Job Summary: 

• Lead the team to ensure the rapid ingestion of data into the Global data platforms
• Coach, grow and develop the team of Data Engineers working with data integration technologies for Big Data and traditional data sources
• Design, review and ensure the scalability of data and events ingested
• Implement, test and document components to ingest data to support new features
• Provide escalation support for the data ingestion portion of the platform
• Closely collaborate with the data analytics community to integrate new data sources in the platform as well as with operations teams to develop maintenance and operational procedures, as well as escalation paths
• Research and validate data analytics and insight concepts defined together with business and technology stakeholders
• Work together with other engineering leadership members to create technology roadmaps and architectures
• Work together with product management and influence product roadmaps
• Work together with other product/feature team members to deliver the product outcomes
• Introduce new technologies and concepts that improve the ways of working or the technology stack
• Coach the more junior members of the team
• Work together with the team leads to develop the team’s technical competencies further

Skills on Resume: 

• Data Ingestion (Hard Skills)
• Team Coaching (Soft Skills)
• Scalable Design (Hard Skills)
• Component Implementation (Hard Skills)
• Escalation Support (Soft Skills)
• Cross-Team Collaboration (Soft Skills)
• Roadmap Planning (Soft Skills)
• Tech Innovation (Hard Skills)

Job Summary: 

• Perform data ETL on various Mastercard data sources and ensure the fidelity of the data sources
• Drive the discussions and problem research with the key stakeholders, and transform business questions into data-driven solutions
• Work closely with key stakeholders, gathering requirements to plan, build and run dashboards to help drive business decisions
• Technical owner of the analytics solutions in the customer intelligence team and naturally the expert on the advanced analytics models created
• Help drive customer intelligence strategy by providing solutions and automation
• Use advanced analytics techniques to create new ways of providing insights into customer behaviours
• Work cross-functionally with business partners to ensure understanding of operational and qualitative key performance indicators
• Develop a working knowledge of MasterCard products and customer touch points
• Prepare and present reporting and analysis to management and internal business partners and senior management
• Technically supervise junior data scientists and analysts on the team

Skills on Resume: 

• Data ETL (Hard Skills)
• Stakeholder Research (Soft Skills)
• Dashboard Development (Hard Skills)
• Solution Ownership (Hard Skills)
• Automation Strategy (Hard Skills)
• Behavior Insights (Hard Skills)
• KPI Alignment (Soft Skills)
• Team Supervision (Soft Skills)

Job Summary: 

• Analyze large datasets and identify meaningful patterns that provide actionable insights
• Execute analytical experiments to help solve various problems and make a true impact across various domains including consumer, product, supply chain and sales
• Apply analytical methods to solve complex, data-driven problems that will ultimately have a direct impact on the business
• Explain complex modeling approaches in simple terms and develop compelling narratives that connect modeling results with business problems
• Partner with a cross-functional team to streamline data science solutions
• Operationalize and automate machine learning models, driving seamless production practices from both business and technology perspectives
• Create data mining and analytics architectures, coding standards, statistical reporting, and data analysis methodologies with the help of the team
• Provide and apply quality assurance best practices for data science services across the organization
• Assist in the development of data management policies and procedures
• Develop best practices for analytics instrumentation and experimentation

Skills on Resume: 

• Data Analysis (Hard Skills)
• Experiment Execution (Hard Skills)
• Problem Solving (Hard Skills)
• Insight Communication (Soft Skills)
• Cross-Functional Partnership (Soft Skills)
• Model Operationalization (Hard Skills)
• Quality Assurance (Hard Skills)
• Best Practice Development (Soft Skills)

Job Summary: 

• Gather, shape, and interpret customer requirements to design and create analytical solutions
• Undertake a detailed analysis that will drive meaningful results
• Provide insights to senior stakeholders
• Build algorithms and optimization tools to be deployed for use by the business
• Design and build data models that support business processes but are agnostic of ever-changing source systems
• Grow and drive the organisation's data analytics strategy
• Develop and implement tools and processes that inform business decisions
• Support sales and product to identify opportunities to win new business

Skills on Resume: 

• Requirement Gathering (Soft Skills)
• Data Analysis (Hard Skills)
• Stakeholder Insight (Soft Skills)
• Algorithm Development (Hard Skills)
• Model Design (Hard Skills)
• Strategy Growth (Soft Skills)
• Decision Tools (Hard Skills)
• Business Support (Soft Skills)

Job Summary: 

• Lead the high-quality and efficient development of AI capabilities, based on large and complex data sets, across multiple projects and product lines, to meet customer requirements
• Responsible for effective technical management and delivery of high-value projects
• Work with senior colleagues across the business to agree desired outcomes and prioritise deliverables, taking into account considerations such as transparency, accountability, fairness, trustworthiness and privacy
• Collaborate with data governance, legal and responsible innovation teams
• Ensure safe development of AI/DS applications, with appropriate ongoing governance
• Contribute to the development of and ensure adherence to relevant frameworks and regulations to ensure safe development of AI and DS applications
• Collaborate with the Applied Research team to harness the latest developments in AI and data
• Support business case development, tendering, budgeting and forecasting processes

Skills on Resume: 

• AI Development (Hard Skills)
• Project Management (Soft Skills)
• Outcome Alignment (Soft Skills)
• Cross-Functional Collaboration (Soft Skills)
• Safe Development (Hard Skills)
• Regulation Adherence (Hard Skills)
• Research Collaboration (Soft Skills)
• Business Support (Soft Skills)

Job Summary: 

• Collaborate with cross-functional agile teams of data scientists, machine learning engineers, software engineers, and others to build a machine learning infrastructure that best supports The Post’s ML needs
• Identify, scope, design and deliver ML feature pipelines
• Apply machine learning technologies to build statistical models with large amounts of data and analyze that data to derive valuable insights and inform feature and product development
• Drive and document best practices in ML systems and data engineering
• Optimize pipelines and processes for model development, experimentation, and scaled production scoring
• Deploy ML models under the constraints of scalability, correctness, and maintainability
• Monitor and ensure the quality of machine learning solutions by implementing process and control disciplines as well as tooling to govern
• Partner with stakeholders across the organization to identify business needs and align data products with business goals
• Actively identify and resolve strategic issues that may impair the team’s ability to meet strategic, scientific, and technical goals
• Partner with research stakeholders to create, maintain, and prioritize the hypothesis and experimentation backlog
• Mentor and train lower-level data scientists and machine learning engineers

Skills on Resume: 

• Team Collaboration (Soft Skills)
• Pipeline Design (Hard Skills)
• Statistical Modeling (Hard Skills)
• Best Practice Development (Soft Skills)
• Pipeline Optimization (Hard Skills)
• Model Deployment (Hard Skills)
• Solution Monitoring (Hard Skills)
• Stakeholder Alignment (Soft Skills)

Job Summary: 

• Identify and scale best practices (cross-enterprise teams/external partners) and advanced models/techniques - harnessing AI/Machine Learning 
• Work with Global and Regional teams to collect data, design, build, and implement AI/ML models to support Vision Care initiatives and improve their outcomes
• Research and evaluate innovative analytical methodologies, approaches, and solutions
• Interpret and communicate analytic results to analytical and non-analytical business partners
• Make decisions regarding own work methods, sometimes in ambiguous situations, and requires minimal direction and receives guidance 
• Partner with regional markets and business customers to identify, align, and prioritize relevant and high-value data for analytical consumption
• Partner with IT to enable a sustainable life cycle journey for prioritized data, including ingestion, cataloguing, data quality controls, and data governance
• Uncover new high-potential untapped data sources for use and sourcing, including JNJ enterprise data and 3rd part data

Skills on Resume: 

• Best Practice Scaling (Soft Skills)
• Model Implementation (Hard Skills)
• Methodology Research (Hard Skills)
• Result Communication (Soft Skills)
• Independent Decisioning (Soft Skills)
• Data Prioritization (Hard Skills)
• Data Governance (Hard Skills)
• Data Sourcing (Hard Skills)

Job Summary: 

• Build machine learning algorithms that discover and answer challenging business questions
• Manage a team of professional data scientists and data analysts to create actionable insights and business intelligence for the business, and that inform company strategy and team execution
• Create compelling, persuasive presentation materials to communicate key learnings and recommendations to the stakeholders clearly and concisely
• Set the strategy and roadmap for the Analytics team
• Assess high-impact business opportunities and partner with other leaders to understand their analytical needs and then develop reports/dashboards that meet those needs
• Track and analyze large amounts of data to discover meaningful trends and present to key stakeholders of the organization every week
• Provide reliable and insightful data on user acquisition, media cost, attribution and expected player retention so PWE can acquire more of the right kind of player
• Provide data and actionable insights on player engagement and campaign performance across messaging channels and the website

Skills on Resume: 

• Algorithm Building (Hard Skills)
• Team Management (Soft Skills)
• Insight Presentation (Soft Skills)
• Analytics Strategy (Soft Skills)
• Opportunity Assessment (Hard Skills)
• Trend Analysis (Hard Skills)
• User Insights (Hard Skills)
• Engagement Analytics (Hard Skills)

Job Summary: 

• Partner with data engineers to develop models that can increase the effectiveness of acquisition and retention marketing, predict churn and player behavior, and increase the accuracy of forecasts and performance reports
• Support the marketing team with reporting, analytical insights and make strategic recommendations related to the media performance and marketing support for each market and campaign
• Identify player trends and recommend data-driven responses to help guide strategy and decision making for market expansion, customer and revenue growth
• Devise and run A/B tests to, directly and indirectly, drive KPI improvements
• Support game development teams with in-game analytics and reporting
• Provide valuable insights related to player behavior, progression, game economy, item drop, and other in-game activities
• Assist in the development of new reporting standards and methods for all aspects of the organization
• Manage dashboards and analytics that communicate status and evaluate the effectiveness of promotions, offers, and campaigns
• Stay abreast of industry trends and innovative developments

Skills on Resume: 

• Model Development (Hard Skills)
• Marketing Support (Soft Skills)
• Trend Identification (Hard Skills)
• Test Execution (Hard Skills)
• Game Analytics (Hard Skills)
• Behavior Insights (Hard Skills)
• Reporting Standards (Hard Skills)
• Dashboard Management (Hard Skills)

Job Summary: 

• Lead a team of enthusiastic data scientists and machine learning engineers and provide necessary thought leadership to solve business problems
• Actively work with the product management team, business leaders and other stakeholders to understand the business requirements and transform them into data and ML requirements
• Responsible for the conception, planning and prioritisation of data science technologies used to produce the desired output in line with the business unit goals
• Collaborate with application developers and DevOps engineers to deploy necessary ML models in production on time with quality
• Utilize appropriate data analytics tools to give high-level insights to the stakeholders
• Constantly mentor and foster innovation with the data science team
• Adhere to coding guidelines as prescribed by the company and ensure they are strictly followed within the team
• Actively engage with Project managers to make sure data science projects are being properly tracked and documented in JIRA
• Make sure all the design/solution approaches for all the data projects are well documented in JIRA
• Actively engage with QA managers and ensure to have a proper test plan and execution towards maximum test coverage
• Strive for test automation to optimize the overall project execution time

Skills on Resume: 

• Team Leadership (Soft Skills)
• Requirement Translation (Soft Skills)
• Technology Planning (Hard Skills)
• Model Deployment (Hard Skills)
• Data Insights (Hard Skills)
• Innovation Mentoring (Soft Skills)
• Coding Compliance (Hard Skills)
• Test Automation (Hard Skills)

Job Summary: 

• Lead ML feature engineering efforts through scientific research
• Design and implement experiments to produce actionable insights and improve model performance
• Collaborate with other data scientists and engineers to productionise ML features/models
• Write high-quality Python for feature engineering and model training
• Work with business stakeholders to define business requirements including KPI and acceptance criteria
• Lead and work with ML engineers and data scientists to develop a recommendation system using advanced machine learning techniques such as deep learning and reinforcement learning
• Lead research initiatives into state-of-the-art methodologies that will enhance current models and power future personalized models
• Collaborate with data engineers, ML engineers, and Data Scientists in building real-time and batch machine learning pipelines that include data preprocessing, feature engineering, model training, model validation, serving, and evaluating results of A/B test
• Drive work on improving the codebase and machine learning lifecycle infrastructure

Skills on Resume: 

• Feature Engineering (Hard Skills)
• Experiment Design (Hard Skills)
• Model Production (Hard Skills)
• Python Programming (Hard Skills)
• Requirement Definition (Soft Skills)
• Recommendation Modeling (Hard Skills)
• Methodology Research (Hard Skills)
• Pipeline Development (Hard Skills)