• Develop custom data models and algorithms for application to data sets.
• Participate in the development of documentation.
• Coordinate with customers, Scrum Masters, and cross-functional areas to communicate project statuses and initiatives.
• Analyze data to coordinate the installation of new systems or modifications to existing systems.
• Support the Agile software development lifecycle.
• Communicate key project data to team members and foster team cohesion and effectiveness.
• Develop and execute project plans using the Atlassian tool suite, including JIRA and Confluence, to track activities.
• Apply best practices and standard operating procedures.

Skills: Data Modeling, Algorithm Development, Agile Methodology, JIRA Confluence, Technical Documentation, Systems Analysis, Cross-Functional Coordination, Project Planning

• Extract insights from data to enable data-driven strategic thinking across the entire value chain.
• Analyze information to support informed decision-making at different stages of the business process.
• Support team activities related to data collection and preparation.
• Integrate cross-channel data to create a comprehensive analytical view.
• Perform data cleansing to ensure accuracy, consistency, and reliability.
• Explore and visualize data to uncover patterns, trends, and opportunities.
• Contribute to competitive intelligence through structured data analysis.
• Design and manage experiments and analytics projects aligned with specific business objectives.
• Support the expansion and effective use of existing modeling and analytical tools.

Skills: Data Analysis, Data Visualization, Data Cleansing, Cross-Channel Data Integration, Experimental Design, Analytical Modeling, Competitive Intelligence, Data-Driven Decision Making

• Receive project briefs from the Head of BI and translate them into structured analytical workstreams.
• Conduct robust analyses following a defined process from data extraction through to result communication.
• Validate data to ensure accuracy, completeness, and suitability for analysis.
• Perform data exploration and preprocessing to prepare datasets for modeling.
• Select relevant variables and choose appropriate modeling techniques.
• Evaluate and optimize models to ensure strong performance and reliability.
• Communicate model outputs clearly to support business understanding and decision-making.
• Work closely with a Senior Analyst within the Decision Science team.
• Build and maintain propensity models for campaign targeting and prospect scoring.
• Support customer churn modeling initiatives.
• Contribute to attribution and media mix modeling activities.
• Develop automated and repeatable processes for regression and statistical inference on an ad hoc basis.

Skills: Advanced Data Analysis, Statistical Modeling, Data Validation, Data Preprocessing, Predictive Modeling, Model Optimization, Business Communication, Regression Analysis

• Perform statistical analyses to identify new and relevant business indicators and emerging trends.
• Analyze diverse datasets to define data-driven solutions to complex business challenges.
• Apply appropriate statistical techniques to support insight generation and decision-making.
• Ensure high data quality across all stages of data acquisition and processing.
• Oversee data sourcing, collection, and ground truth generation activities.
• Normalize and transform data to ensure consistency and analytical readiness.
• Collaborate with engineers to build and enhance predictive models for targeted solutions.
• Support the development of efficient data querying and modeling infrastructure.
• Create clear and impactful data visualizations to present analytical results.
• Communicate findings effectively through structured documents and presentations.
• Validate consumer panel weighting approaches to improve data stability and accuracy.

Skills: Statistical Analysis, Data Quality Management, Data Normalization, Predictive Modeling, Data Visualization, Insight Generation, Cross-Functional Collaboration, Analytical Reporting

• Gather historical data from multiple sources to support analytical initiatives.
• Process and analyze historical datasets to identify meaningful patterns and trends.
• Apply statistical analysis and artificial intelligence techniques to transform data into actionable insights.
• Support data-driven business decision-making through advanced analytics.
• Build clear and reliable reports to communicate analytical results.
• Develop interactive dashboards to present findings to stakeholders.
• Design and develop data warehouses to support scalable analytics.
• Build and maintain data pipelines to ensure efficient data flow and availability.
• Assist senior team members with research and development on new AI use cases.
• Support initiatives related to emerging AI technologies in finance and banking.
• Collaborate with engineers to enhance and maintain proprietary AI products.

Skills: Data Analysis, Statistical Analysis, Artificial Intelligence, Data Visualization, Reporting Dashboards, Data Warehousing, Data Pipelines, Cross-Functional Collaboration

• Design and develop machine learning models for text analytics tasks such as extraction, disambiguation, and classification.
• Evaluate model performance to ensure accuracy and relevance for business use cases.
• Select modeling approaches that align with specific business problems.
• Contribute to the development of custom algorithms when standard approaches are insufficient.
• Design solutions that balance technical robustness with practical business needs.
• Develop new features for existing products based on analytical and technical requirements.
• Implement proofs of concept and prototypes to validate ideas and approaches.
• Collaborate closely with product development teams within an Agile framework.
• Engage with product stakeholders to align development with product goals.
• Communicate effectively with technologists and non-technical stakeholders.

Skills: Machine Learning Modeling, Text Analytics, Model Evaluation, Algorithm Development, Feature Engineering, Proof Of Concept Development, Agile Collaboration, Stakeholder Communication

• Develop analytical models and business scenarios using data science techniques.
• Acquire data from various sources and identify, analyze, and interpret trends or patterns in data sets.
• Develop reports and present data using various visualization tools.
• Support advanced analytics and innovation projects and big data-related application development.
• Collaborate with the IT leadership team and project managers.
• Work with the data architect, business system solution leaders, and IT business partners.
• Support the digital service platform, including the data platform, collaborative application development on the cloud, and proof of concept of digital solutions.
• Coordinate with the middleware team.
• Collaborate with mobile and platform development teams.
• Manage time and tasks independently for effective delivery.
• Articulate needs, risks, and opportunities clearly and objectively.
• Communicate proactively with business stakeholders to provide insights and gather feedback.
• Maintain a strong interest in understanding business challenges and opportunities enabled by advanced technologies.

Skills: Data Science Modeling, Data Acquisition, Data Visualization, Advanced Analytics, Big Data Applications, Cross-Functional Collaboration, Cloud Platforms, Stakeholder Communication

• Take ownership of reporting projects to identify areas of productivity and cost-saving improvements.
• Perform data mining, analysis, and create visualizations using SQL, Excel, and Power BI.
• Analyze and document business processes and metrics to identify improvement opportunities.
• Streamline routine analysis and reports.
• Prepare industry and competitive analysis to benchmark business objectives against the market.
• Deliver results with accuracy and urgency across multiple projects.
• Provide quality customer service to a diverse group of internal customers.
• Collaborate effectively with a wide range of individuals to build trust, capability, and a strong data community.

Skills: Reporting Ownership, Data Mining, SQL Analysis, Excel-Power BI, Process Improvement, Competitive Analysis, Data Visualization, Stakeholder Collaboration

• Partner with business and technical stakeholders to translate complex business challenges into effective data science solutions.
• Work closely with data engineers, data stewards, product owners, and business users throughout project delivery.
• Support the full data science lifecycle from initial data wrangling to operational deployment.
• Conduct exploratory data analysis to understand data structure, quality, and potential insights.
• Formulate and test hypotheses to guide analytical direction and model design.
• Develop and validate models through iterative prototyping and business testing.
• Apply advanced analytical techniques such as predictive modeling and machine learning.
• Use time series analysis, simulation, and optimization to address complex analytical problems.
• Leverage large volumes of structured and unstructured data to generate meaningful insights.
• Prepare high-quality information sets suitable for robust modeling and analysis.
• Communicate complex analytical outcomes clearly to both technical and non-technical stakeholders.
• Maintain up-to-date knowledge of emerging data science techniques, technologies, and ML/AI applications.

Skills: Data Science Solutions, Stakeholder Collaboration, Data Wrangling, Exploratory Data Analysis, Predictive Modeling, Machine Learning, Time Series Analysis, Analytical Communication

• Apply advanced data and technical skills to perform analysis, data mining, and visualization.
• Use Business Intelligence tools and data development platforms to deliver analytical insights.
• Assess and validate data quality using SQL and appropriate data analysis techniques.
• Improve client reporting capabilities through reliable and well-structured data outputs.
• Stay informed on emerging data science and technology trends.
• Advise clients on how new technologies can support future platform development.
• Actively participate in team-based problem-solving initiatives.
• Contribute ideas and solutions to address client challenges effectively.
• Understand business and technical data requirements in depth.
• Design and build data pipelines to automate and accelerate data preparation processes.

Skills: Data Analysis, Data Mining, Data Visualization, Business Intelligence Tools, Data Quality Validation, Client Reporting, Data Pipeline Design, Stakeholder Consulting

• Build predictive and machine learning models using techniques such as neural networks, decision trees, ensemble methods, and clustering.
• Select appropriate algorithms based on data characteristics and business objectives.
• Continuously innovate by creating and engineering new variables to enhance model performance.
• Evaluate the impact of feature engineering on model accuracy and robustness.
• Improve modeling techniques through tuning, validation, and performance optimization.
• Maintain and refine existing models to ensure long-term reliability and scalability.
• Standardize and enhance procedures for building advanced analytical modeling processes.
• Analyze large volumes of IoT data to extract meaningful insights.
• Translate complex IoT data patterns into actionable analytical outcomes.

Skills: Predictive Modeling, Machine Learning Techniques, Algorithm Selection, Feature Engineering, Model Optimization, Model Maintenance, Analytical Process Standardization, IoT Data Analysis

• Scope, develop, and implement machine learning and scorecard-based models in line with banking governance and best-practice standards.
• Ensure models comply with regulatory, risk, and validation requirements.
• Use strong SQL and Python skills to extract, process, and analyze large datasets.
• Define and engineer features that capture evolving fraud behaviors.
• Identify fraud patterns both directly and through signals from affected customers.
• Apply analytical curiosity and problem-solving skills to turn uncertainty into value-generating insights.
• Investigate ambiguous data scenarios to uncover hidden risks and opportunities.
• Craft clear and compelling data-driven narratives for executive and external audiences.
• Deliver high-quality presentations that translate complex analytics into business impact.
• Leverage BI tools such as Superset, Tableau, and Power BI to detect anomalies and trends.
• Develop deep domain knowledge across cybercrime areas, including scams, CNP fraud, and money laundering.

Skills: Machine Learning Modeling, Regulatory Compliance, SQL-Python Analytics, Fraud Feature Engineering, Fraud Pattern Detection, Analytical Problem Solving, Data-Driven Storytelling, BI Tools Analysis

• Analyze large and complex datasets to extract meaningful insights.
• Interpret analytical results and present findings clearly to stakeholders.
• Leverage data analysis to identify and quantify data quality issues.
• Assess the impact of data quality on reporting and decision-making.
• Monitor data exceptions to detect anomalies and inconsistencies.
• Report identified exceptions to trigger timely remediation actions.
• Develop clear metrics to track data quality and performance.
• Produce management reports that support oversight and informed decisions.
• Support the definition and adoption of standards that ensure consistently high data quality.

Skills: Data Analysis, Insight Interpretation, Data Quality Assessment, Anomaly Detection, Exception Monitoring, Data Quality Metrics, Management Reporting, Data Governance

• Design and perform manual data analysis on live production data to detect data anomalies and support anti-fraud purposes.
• Monitor releases and development activities to anticipate potential future issues.
• Develop and maintain test plans, test cases, and checklists related to anti-fraud and compliance.
• Perform manual acceptance testing of new features before product releases.
• Stay informed of configuration, deployment, and database changes to ensure analytics tools function as expected.
• Ensure compatibility of fraud detection solutions with future browser and operating system releases.
• Conduct data analysis and visualization, review production statistics, and forecast potential problem areas.
• Review and analyze existing anti-fraud dashboards, tools, and reports to improve manual review methodologies.
• Research and investigate new geo-blocking bypass methodologies.
• Develop and maintain a thorough understanding of company technologies, processes, and teams.
• Understand the company vision, goals, and strategy and actively contribute to achieving goals and executing strategy.
• Accurately report hours spent on tasks in Jira.

Skills: Fraud Data Analysis, Anomaly Detection, Manual Testing, Test Case Development, Data Visualization, Production Monitoring, Anti-Fraud Tools, JIRA Tracking

• Develop timely and accurate reports to support infrastructure decision-making.
• Maintain existing reporting processes and manage deadlines for report modifications.
• Ensure reporting outputs remain consistent, reliable, and up to date.
• Perform proactive analysis on infrastructure performance metrics.
• Forecast key indicators such as Homes Passed and other operational KPIs.
• Analyze trends to anticipate future infrastructure needs and constraints.
• Provide data-driven guidance to support infrastructure strategy and planning.
• Translate complex analysis into clear, actionable conclusions.
• Communicate insights and learnings effectively to stakeholders.
• Drive high data quality standards for all data used in reporting and analysis.
• Initiate Business Intelligence developments to enhance infrastructure planning and strategy execution.

Skills: Infrastructure Reporting, Performance Analysis, KPI Forecasting, Trend Analysis, Strategic Planning, Business Intelligence Development, Data Quality Management, Stakeholder Communication

• Complete basic and advanced desk research and analysis on company management of environmental and social risks and compliance with international norms on responsible business conduct.
• Learn about environmental, social, and governance trends and issues, and acquire specific knowledge about dominant issues across various industries.
• Attend to internal and client queries and requests to ensure data captured aligns with data methodology and policy guidelines.
• Comply with established work process standards to ensure the quality of data collected.
• Meet pre-determined turnaround goals for assigned work.
• Escalate data interpretation issues.
• Perform root-cause analysis when data issues are identified.
• Update internal documents for performance metrics monitoring.
• Participate in working committees, projects, or tasks beyond core responsibilities.
• Maintain appropriate paper and electronic files in accordance with file retention policies.

Skills: ESG Research, Environmental Social Risk Analysis, Regulatory Compliance, Data Quality Assurance, Root Cause Analysis, Policy Adherence, Performance Metrics Monitoring, Stakeholder Support

• Work with data science techniques and libraries to demonstrate data’s applicability to business problems.
• Perform exploratory data analysis to define analytical models.
• Develop advanced quantitative models using various programs and software to support predictive assessments.
• Provide expertise for analytical, programmatic, strategic, and operational analysis.
• Explain analytics model behavior and results in business terminology.
• Perform technical risk analysis and reliability assessments.
• Conduct multiple forms of advanced analyses, including sustainment, optimization, text analytics, machine learning, and parametric and non-parametric statistical models and techniques.
• Build, test, validate, and demonstrate analytical models using relevant error metrics and calibration techniques.
• Provide recommendations for plans, programs, strategies, policies, and budgets.
• Design algorithms that utilize multiple models or methods within an ensemble framework.
• Deploy models into production.

Skills: Data Science Techniques, Exploratory Data Analysis, Quantitative Modeling, Predictive Analytics, Risk Analysis, Machine Learning Models, Algorithm Design, Model Deployment

• Work with the team and client to shape and develop requirements for answering complex life science questions, such as predicting rare disease diagnoses or determining optimal dosing frequencies.
• Address those requirements by developing and applying suitable ETL processes for feature creation from raw data and unbiased machine learning methodologies for predictive modeling.
• Research machine learning techniques to enhance the overall offering.
• Communicate technical concepts and analytical outputs to a diverse group of internal and external stakeholders.
• Own the technical delivery of client-facing projects by collaborating with data engineers, data scientists, and consultants in an agile environment to produce high-quality and robust insights.
• Contribute to code reviews during projects and provide constructive feedback.
• Proactively identify opportunities for innovation and suggest improvements across the workflow.
• Manage multiple priorities effectively in a fast-paced environment with shifting demands.

Skills: Requirements Analysis, ETL Engineering, Machine Learning, Technical Research, Stakeholder Communication, Agile Delivery, Code Review, Workflow Optimization

• Work with team members and the team director to prioritize needs, develop comprehensive and efficient solutions, and translate business problems into data-driven insights.
• Understand a wide range of data sets to mine, analyze, run forecasting models, and create segments to drive insights and develop new data delivery tools.
• Understand business needs and existing data assets to connect insights and support data delivery in targeted digital programmatic buys, partnership deal strategies, windowing, and cross-platform measurement.
• Aggregate data to create predictive tools for audience growth, programming opportunities, and optimal marketing plans.
• Develop normative databases from existing survey data.
• Create data maps and taxonomies from disparate data sets.
• Define key KPIs from merged data sets and translate findings into standard metric deliverables.
• Create processes and techniques to reliably fuse custom survey data with syndicated measurement data and merge different measurement data sets.
• Work with the director and team to build a consolidated data platform hosting statistical algorithms for predictive modeling, data visualization, dashboarding, and real-time flexible reporting.

Skills: Data Prioritization, Predictive Modeling, Forecasting Analysis, Audience Segmentation, Data Integration, KPI Definition, Survey Data Fusion, Data Platform Development

• Be part of a group of data scientists applying state-of-the-art analytics and ETL to challenges in distribution, life, and financial services.
• Leverage customer information, including macro and micro transactional behavioral data, to influence strategic business decisions using complex analytics, multivariate models, machine learning, and data mining technologies.
• Help operationalize business decisions across initiatives.
• Promote a measure of success culture focused on profitable growth initiatives.
• Contribute to the expansion and deepening of data science toolkits.
• Enhance understanding of market intelligence by integrating business, strategy, and IT implications into initiatives.
• Perform advanced modeling, programming, and ETL to drive projects forward.
• Utilize tools and languages such as Python, GitHub, machine learning frameworks, multivariate modeling, APIs, SQL, and ETL software.
• Drive best practices in software and hardware utilization.
• Support complex and ambiguous business challenges involving data science applications.
• Define and understand project scope, including measures of success and a data science project vision and roadmap.
• Execute project plans with urgency and deliver results by target dates.
• Partner with IT, business, and data management teams to leverage data infrastructure for improved information ingestion and operationalization of analytics outputs.
• Develop deep knowledge of consumer analytics, including closure models, retention models, agency economics, and lead optimization.
• Collaborate with business partners, executives, and other leaders to assess analytical needs for projects.
• Support fact-based decision making and reinforce a culture focused on measurable success.
• Present complex information clearly to technical and non-technical audiences at all project levels using tools such as PowerPoint.

Skills: Advanced Analytics, ETL Development, Machine Learning, Data Mining, Predictive Modeling, Consumer Analytics, Cross-Functional Collaboration, Data Science Delivery

• Experience in applying data science techniques to present, communicate, and disseminate data to achieve and measure impact whilst maintaining user focus.
• Understanding of the ethical considerations of potential data science approaches, and the legislation applicable in this area, i.e., GDPR, DPA, etc.
• Experience in using Java, Python, R, R Shiny, or other programming languages.
• Strong curiosity through proactively researching and experimenting with new programming and machine learning tools and techniques.
• Experience working within an agile team and being involved in leading project tasks and assisting with sprint planning.
• Experience in leading small-scale data science and machine learning projects, and a desire to do more of it.
• Strong analytical thinking with the ability to break down complex problems into manageable steps.
• Attention to detail when working with data, models, and documentation.
• Ability to prioritize tasks and manage time effectively in a fast-paced environment.
• Clear written communication skills.

Qualifications: BS in Data Science with 3 years of Experience

• Experience in a statistical and/or data science/business intelligence role.
• Experience in statistics, optimization, or related fields.
• Deeper experience in working with large data sets, simulation/optimization, or distributed computing tools.
• Experience with programming, specific experience in R or Python.
• Experience in working with large data sets, simulation/optimization, and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, etc.).
• Strong analytical and problem-solving skills.
• Structured thinking to approach ambiguous or loosely defined problems.
• Persistence in investigating complex data or performance issues.
• Ability to balance accuracy and practicality when delivering insights.
• Professional judgement in selecting appropriate analytical approaches.

Qualifications: BA in Statistics with 5 years of Experience

• Practical experience with data visualization tools, such as Tableau, Power BI, and D3.js.
• Demonstratable understanding and practical experience with a wide range of Machine Learning and Statistical Modeling algorithms/techniques.
• Experience in writing clean and efficient R and/or Python code for data cleansing and modeling (experience in other programming languages as well).
• Ability and passion to learn any new tool/language/algorithm on the fly.
• Good understanding of RDMS and SQL, and meaningful exposure to cloud solutions.
• Experience with Microsoft Azure cloud solutions.
• Passion for creating business value through data science.
• Great communication and presentation skills.

Qualifications: BS in Computer Science with 1 year of Experience

• Work experience in data analytics, consulting, or data science.
• Proficient in computer skills and flexible in coding.
• Good knowledge of Python with exposure to machine learning and SQL.
• Optional knowledge in GIS, big data frameworks like PySpark, Apache Spark framework.
• Exposure to AWS services.
• Creative data visualization, data extrapolation, and storytelling skills.
• Excellent reporting and verbal communication skills.
• Passionate to learn new technologies and developing creative ideas.
• Self-directed and able to work without supervision.

Qualifications: BS in Business Analytics with 2 years of Experience

• Experience working on Analytics and Business Intelligence-focused initiatives, preferably in a consulting capacity.
• Experience using Python, R, or other languages to build statistical models or analyze data.
• Experience in creating complex SQL queries and functions, data structures.
• Strong analytical problem-solving skills.
• Experience working with various self-service business intelligence and data visualization tools such as Tableau, Power BI, Business Objects, etc.
• Experience working in an Agile-based environment.
• Strong technical and troubleshooting techniques.
• Ability to translate business questions into analytical tasks.
• Strong collaboration skills when working with cross-functional teams.
• Adaptability to changing project requirements and priorities.
• Proactive mindset in identifying opportunities for process or analysis improvement.

Qualifications: BA in Applied Mathematics with 4 years of Experience

• Exposure to Python, R, Java, C++, C, Angular, HTML.
• Experience working on public cloud (AWS, Azure, or Google Cloud Platform).
• Knowledge of SAS SQL Databases, database modeling, and other querying languages.
• Experience in writing solutions using Python SQL.
• Working knowledge of Text Analytics and Natural Language Processing, Sequence Labeling.
• Knowledge of Data Science, Artificial IntelligenceMachine learning, Probabilistic and Statistical Learning Methods.
• Strong communication and cross-functional collaboration skills.
• Ability to explain technical concepts clearly to both technical and non-technical stakeholders.
• Strong organizational skills when managing multiple tasks or deliverables.
• Willingness to learn new tools, frameworks, and methodologies quickly.

Qualifications: BS in Computational Science with 3 years of Experience

• Strong grasp of probability, statistical inference, optimization algorithms, linear algebra, and calculus.
• Understanding of how and when to apply key analytical approaches, including regression analysis and machine learning.
• Good grasp of techniques for model cross-validation and penalizing unnecessary complexity, including regularization, in-sample vs out-of-sample.
• Ability to write near-production-level code in at least one general-purpose programming language (e.g., Python, C++).
• Flexible and adaptable to learn and understand new technologies.
• Demonstrable analytical and problem-solving abilities, coupled with an enquiring mind and the ability to learn quickly.
• A keen eye for detail, good at spotting problems, and quickly proposing solutions.
• Strong verbal and written communication skills (fluency in English).
• Experience with natural language processing techniques and tools for the parsing of unstructured data.
• Experience with deep learning and other advanced modeling approaches to extract and automate insights from data.
• Fluency with statistical significance tests and Monte Carlo simulations.
• Experience with automation packages, such as Luigi.
• Exposure to Linux.

Qualifications: BS in Information Systems with 5 years of Experience

• Passionate about the transformative impact the right information can have on a business, and already experimented with how to gain insight from data.
• Be strongly numerically disciplined, or able to demonstrate self-guided learning, which has provided a foundation for quantitative analysis.
• Have an appreciation of statistical methods, experimental design, and the underlying principles of machine learning algorithms.
• Keen to learn how to transform, analyse, and model data from a variety of data sources, extracting and interpreting trends and insights.
• Comfortable explaining technical concepts to non-technical audiences, be prepared to participate in meetings and manage situations with imperfect information.
• Curiosity about Natural Language Processing.
• A basic grasp of R or Python.
• Intellectual curiosity and motivation to explore unfamiliar problem spaces.
• Ability to work productively despite incomplete or evolving requirements.
• Clear verbal communication skills.

Qualifications: BS in Data Engineering with 1 year of Experience

• Advanced knowledge of VBA with a good understanding of SQL and relational databases.
• Willingness to learn tools such as Python, R, ERP systems, Azure, Alteryx, or Power BI.
• Interest in supply chain and data science topics, including planning processes, ERP systems, performance measurement, and the creation of onboarding or training materials.
• Proactive and results-driven mindset with the ability to work effectively as part of a collaborative team.
• Strong attention to detail with the ability to work accurately on large and complex datasets containing technical terminology.
• Ability to identify data inconsistencies and potential errors while maintaining focus on key tasks and deliverables.
• High motivation to contribute to overall business objectives and continuously improve outcomes across the organization.
• Strong self-motivation to support project delivery and assist stakeholders throughout change initiatives.
• Ability to adapt to organizational culture and work effectively within dynamic business environments.
• Basic project management knowledge with an understanding of Agile ways of working.
• Capability to operate across different functions and cultures while building effective working relationships.
• Good written and verbal English communication skills.

Qualifications: BA in Supply Chain Analytics with 2 years of Experience

• Professional work or a corresponding amount of coursework experience with the following Database programming.
• Understanding of relational and dimensional data modeling concepts.
• SQL development experience (SQL Server or PostgreSQL).
• Understanding of database security and administration.
• Data processing experience - ability to perform data preparation and transformation tasks using a variety of tools.
• Familiarity with ETL technologies.
• Solid grasp of Python for scripting and automation of repetitive tasks.
• Experience using Jupyter Notebooks and Python IDEs.
• Knowledge of common data science and numerical libraries in Python (e.g., NumPy, SciPy, Pandas) and visualization libraries (e.g., Matplotlib, Seaborn).
• Working knowledge of cloud technologies.
• Experience with using cloud-based machine learning tools, preferably on the Azure platform.

Qualifications: BS in Database Management with 4 years of Experience

• Working knowledge of Data Science/Machine Learning.
• Familiarity with Data Science proficiency in programming languages (e.g., Python).
• Experience with a variety of modern and legacy data platforms, databases, and SQL.
• Experience supporting data migration or integration activities across different data platforms.
• Experience performing data reconciliation and consistency checks between legacy and modern systems.
• Methodical approach to problem investigation and root-cause analysis.
• Ability to adapt quickly when working with unfamiliar data environments.
• Strong attention to accuracy and consistency in analytical work.
• Effective collaboration with technical and non-technical colleagues.
• Willingness to take initiative in improving data processes or analytical practices.

Qualifications: BA in Machine Learning with 3 years of Experience

• Strong programming skills in PythonData manipulation, feature engineering, model tuning, and validation.
• Strong understanding of statistics, such as distributions, experimental design, information, metrics, etc.
• Strong understanding of common machine-learning algorithms, including boosting, regression, and decision trees.
• Experience working with popular ML libraries.
• Curiosity to explore data and the tenacity to get it right.
• Excellent communicator who is able to tailor a narrative to their audience.
• MLOps experience in AWS, experience in credit risk (adjudication, AIRB) or market risk (VaR, ES), Apache Airflow, Kafka, Spark, Flink.
• Structured problem-solving approach when dealing with complex or ambiguous challenges.
• Ability to work effectively under pressure and manage competing priorities.
• Collaborative attitude when working across engineering, risk, and business teams.

Qualifications: BS in Artificial Intelligence with 5 years of Experience

• Experience with Python.
• Experience with SQL and ML toolkit.
• Experience in collecting, profiling, and preparing raw data for downstream analytical tasks.
• Experience with the interpretation of model outputs and translating them into business-relevant insights.
• Familiarity with statistical reporting and result interpretation standards.
• Awareness of different analytical approaches for descriptive, diagnostic, and predictive analysis.
• Ability to drive a project and work both independently and in a team.
• Excellent verbal and written communication in English.
• Ability to structure work and manage priorities effectively.
• Confident in contributing ideas and perspectives during discussions and reviews.

Qualifications: BA in Quantitative Methods with 1 year of Experience

• Experience in data science roles.
• Proficiency in Python and R.
• Working knowledge of Google BigQuery.
• Exposure to a range of Machine Learning, Data Science, or Statistical Modelling techniques, such as Predictive Modelling, Linear/Logistic Regression, Attribution Modelling, Causal Impact Analysis & Customer Segmentation.
• Strong attention to detail.
• Ability to thrive in a dynamic and fast-paced environment.
• Ability to quickly interpret a problem and mobilise to achieve a solution.
• Ability to communicate problems and solutions to internal and external stakeholders.
• Highly motivated.
• Original, creative thinking coupled with a strategic, outcomes-based mindset.

Qualifications: BS in Operations Research with 2 years of Experience

• Experience in analyzing and validating large data sets.
• Experience with applied statistics.
• Experience developing visually compelling and interactive dashboards.
• Experience with SQL, Python, Scala, and Apache Spark.
• Experience in applying machine learning techniques to solve network operations problems.
• Experience in Network Operations.
• Structured approach to breaking down complex operational problems.
• Ability to manage multiple analytical tasks in parallel.
• Clear communication when explaining assumptions, limitations, and findings.
• Strong collaboration skills when working with engineering or operations teams.
• Resilience and adaptability when dealing with noisy or incomplete data.

Qualifications: BA in Statistical Modeling with 4 years of Experience

• Strong statistical/mathematical/computer science skills.
• Solid statistical skills either in survey statistics (sampling, non-response adjustments, Bayesian statistics), or econometric modeling (program evaluation, forecasting, economic theory, panel data), or causal inference.
• Basic knowledge of optimization and/or machine learning algorithms and/or deep learning algorithms.
• Solid knowledge of Python and the respective development tools/IDEs (e.g., Jupyter, PyCharm).
• Solid experience and working knowledge of the constantly evolving Data Science ecosystem and popular machine learning frameworks.
• First experience with common software engineering practices and framework (version control, testing, agile development/Scrum).
• First experience with cloud computing environments (e.g., GCP) and productionizing machine learning models (e.g., exposing as a REST API, containerization, scalability, performance optimization).
• Prior experience in pricing, revenue assurance, and media consumption patterns.
• Ability to think creatively in addressing analytical objectives.
• Ability to fluidly communicate in an English business environment.

Qualifications: BS in Computational Statistics with 3 years of Experience

• Experience with data quality analysis, statistical analysis, and/or modeling, financial or cost analysis, internal controls, and/or external/internal financial audit.
• Experience mining and analyzing electronic data with competency in one or more of the following: SAS, SQL, SPSS, R, Python, ACP, IDEA Tableau, MATLAB, Visual Basic, Business Intelligence, Hadoop, or Alteryx.
• Experience with JavaScript Object Notation (JSON) and Application Programming Interface (API).
• Experience in Data Cleansing and Data Engineering Job Objective.
• Experience using data-oriented programming languages and visualization software.
• Experience applying data mining, data modeling, natural language processing, and machine learning.
• Experience working as a member of a cross-functional team.
• Experience building tools to automate data collection.
• Working knowledge of statistics.
• Good technical writing skills.
• Experience with machine learning and AI.
• Excellent attention to detail.

Qualifications: BS in Information Technology with 5 years of Experience

• Understanding of the theoretical basis for applied practices, and is beginning to be able to apply the theory to practical examples.
• Awareness of the need to translate technical concepts into non-technical language and understand what communication is required to internal and external stakeholders.
• Understanding of the process of software development.
• Basic knowledge of how services/software components are developed and built.
• Experience in using SQL and R/Python.
• Logical and structured thinking when approaching unfamiliar problems.
• Ability to learn quickly from examples, feedback, and practical exposure.
• Attention to detail when following processes and implementing requirements.
• Reliability in meeting commitments and delivering agreed tasks on time.
• Openness to collaboration and knowledge sharing within a team environment.

Qualifications: BS in Software Engineering with 1 year of Experience

• Knowledge of statistical and modeling techniques, including hypothesis testing, dimensionality reduction, supervised learning (classification and regression), forecasting, and unsupervised clustering.
• Knowledge and experience in scripting and programming with Python and SQL.
• Familiarity with data integration patterns and joining heterogeneous data sources.
• Awareness of data lifecycle management, including retention and archival considerations.
• Aptitude for learning and applying new technologies related to Data Science and Data Management.
• Ability to communicate complex analytical concepts and results at multiple levels to both technical and non-technical audiences.
• Ability to structure ambiguous problems into clear analytical questions.
• Confident in asking clarifying questions to ensure analytical relevance.
• Strong stakeholder awareness when tailoring the depth and detail of explanations.
• Proactive approach to continuous learning and skill development.

Qualifications: BA in Data Management with 2 years of Experience

• Familiarity with at least some of the key machine learning and statistical methods and modelling techniques, e.g., time series, regression, clustering, PCA, decision trees, association rules, neural networks.
• Experience in data modelling, data cleansing, and analysis.
• Practical knowledge of R, Python, or SAS (with key libraries and packages).
• Working knowledge of GCP, AWS, Azure, Snowflake, or other cloud providers.
• Experience in Tableau, Power BI, or other visualization tool.
• Working experience with Databricks.
• Working knowledge of GitHub.
• Willingness to learn and broaden functional knowledge (finance, logistics, sales, etc.).
• Strong presentation, communication, and consulting skills.

Qualifications: BS in Predictive Analytics with 4 years of Experience