Data Scientist

• The Strategic Intelligence team transforms complex data into actionable insights that guide technology strategy across Flutter's global portfolio.
• Combine AI-powered analytics, competitive intelligence, and innovation partnerships to help technology leadership anticipate challenges and capitalise on opportunities.
• Building predictive systems, fine-tuning models, and architecting strategic intelligence platforms that power critical decisions across Flutter's technology portfolio.
• Work directly with CTOs, VPs of Engineering, and strategy teams, translating sophisticated analysis into clear, high-impact recommendations.

Senior Data Scientist – Flutter Functions

• Architect data lake/lakehouse platforms for forecasting, anomaly detection, and BI/analytics using Databricks, Spark, and Delta-style patterns.
• Set and enforce engineering standards for data modeling, integration, and pipeline design across teams/products.
• Lead cloud event-driven/microservices architecture that integrates with web front ends and APIs.
• Design, build, and optimize batch and event-driven ELT/ETL pipelines for analytical and operational workloads.
• Build ingestion/transformation flows aligned to bronze/silver/gold, with validation, CI/CD, testing, and observability baked in.
• Implement scalable time-series forecasting training and inference across categories.
• Build model monitoring (RMSE/MAE/bias/coverage/residuals) with dashboards and automated alerts.
• Develop residual/outlier detection using Z-scores, PELT change points, and confidence-interval breach checks.
• Implement classification/risk-scoring models (e.g., Logistic Regression, Random Forest, XGBoost, clustering, HMMs) for anomaly classification and category risk.
• Automate data quality and schema validation (missing dates/targets, type changes, schema evolution, allocation shifts).
• Detect drift in data and model behavior (e.g., allocation stability, category mix changes).
• Deliver per-category anomaly flags and summarized insights for decision-makers.
• Provide technical leadership (code/design reviews, mentoring, knowledge sharing) for engineers and data scientists.
• Collaborate with product/UX/business to refine requirements, prioritize work, and plan roadmaps.
• Champion engineering excellence: quality, performance, and operational readiness.

Data Scientist III

• Design, train, and deploy production ML models for core Sales Cloud use cases: opportunity scoring, deal-risk detection, and seller-facing explainable insights
• Build and operationalize agentic workflows on Agentforce, including LLM-as-judge evaluation frameworks and agentic evaluation metrics
• Partner closely with Product Management to help shape and drive the roadmap — bringing engineering perspective to prioritization and feasibility decisions
• Explore and adopt new technologies: improved forecasting methods, RAG architectures, prompt tuning strategies, and emerging AI tooling
• Mentor and coach junior engineers on the team.

• Manage day-to-day clinical data science activities, supporting your team to deliver quality outcomes.
• Leading the design and implementation of data science strategies to enhance clinical trial data analysis.
• Collaborating with cross-functional teams to identify key data requirements and analytical needs.
• Overseeing the development of statistical models and analytical tools to optimize data interpretation.
• Mentoring and guiding team members in best practices for data analysis and visualization.
• Communicating findings and insights to stakeholders through presentations and reports, influencing project direction.

Clinical Data Science Lead

• Formulates and defines analytics solution objectives and technical requirements based on user needs, an understanding of business processes, industry requirements, and advanced analytic models (statistical, operations research, computing, process).
• Conceptualizes, builds, develops and enhances a client's analytic model.
• Selects the relevant analytic modeling methodology for the use case, available structured and unstructured data, cost and timing constraints to solve the business issue and deliver clear business focused insights.
• Embeds analytic models in an enhanced business process of operational system by collaborating with Application Developers.
• Responsible for measuring business performance based on the model.
• As a fully functioning analytics team member, applies best practices to analytics solutions and contributes to the development of improved best practices.
• Leads the model enhancements.
• Summarizes complex ideas by developing visual models to display insights to simplify user experience.
• Communicate the analytics solution to the appropriate stakeholders.

• Design, develop, and deploy scalable ML / AI solutions that enable natural language-driven automation, insights, and enterprise intelligence.
• Collaborate with UI/UX teams for AI-driven solution delivery

• Analyze behavioural, transactional, and digital data to uncover growth opportunities
• Conduct deep-dives into acquisition performance, benefit adoption, churn risk, and retention drivers
• Identify friction points throughout the membership lifecycle and recommend optimizations
• Create predictive models such as acquisition propensity, churn risk, CLV, and benefit adoption
• Build customer segmentation frameworks to support targeted marketing and personalized experiences
• Partner with engineering and data teams to ensure models are usable and integrated into workflows
• Design, analyze, and interpret A/B tests across product and marketing initiatives
• Apply causal inference techniques to measure impact
• Present insights and recommendations clearly to cross-functional teams
• Develop analytical narratives that shape Membership strategy and roadmap decisions
• Support planning cycles with forecasting, opportunity sizing, and scenario modelling
• Work closely with cross-functional partners across Membership, Product, Marketing, Finance, Operations, and Walmart’s broader Data & Analytics org.

Senior Data Scientist

• Support Empower’s Responsible AI (RAI) program by embedding directly with AI Factory teams
• Ensure AI and GenAI applications are safe, measurable, transparent, and aligned with Empower’s Responsible AI principles
• Serve as the Responsible AI data science partner for assigned AI CoreWorks Factory teams
• Support integration of evaluation workflows, guardrails, and measurement practices
• Execute evaluations related to hallucination, fairness, contextual grounding, transparency, robustness, and other model behavior risks
• Develop benchmark datasets, evaluation scripts, and structured testing methods for consistent assessment across AI applications
• Analyze model architectures, data characteristics, and inference workflows to inform evaluation design and interpretation of results
• Apply machine learning, statistical, and experimental design techniques to assess model behavior and performance
• Evaluate how training approaches, data sources, model configurations, and deployment patterns influence Responsible AI outcomes
• Contribute to research, experimentation, and prototyping of emerging Responsible AI methods and evaluation techniques

• Expected to work in close collaboration with the Client team and EXL team on establishing relationship with stakeholders, identify new opportunities, areas of growth
• Develop long-lasting, trusted advisor relationships with clients
• Analyze structured and unstructured data from a number of drivers in the interest of solving specific questions/problems with processes in the various functional areas
• Ensure Industry best practices are followed, Communicate and follow up with delivery team
• Brainstorming on pain areas/Identifying reasons for issues/Issue Resolution, diagnosis of inefficiencies in the current process and figuring out ways to resolve it
• Facilitate client working sessions and lead recurring project status meetings
• A typical work day will involve working with stakeholders as individual contributor.

Data Scientist – GenAI, Core DS

• Assist in analyzing business processes
• Support development of business requirements
• Document analysis processes
• Perform basic data analysis
• Collaborate with senior analysts.

• Design, develop, and deploy advanced analytics, machine learning, and predictive modeling solutions to solve complex business challenges.
• Lead the development of forecasting, classification, regression, clustering, and optimization models.
• Apply statistical techniques and analytical methodologies to uncover actionable insights from structured and unstructured data.
• Drive the adoption of best practices in data science, machine learning, and model governance.
• Build, train, evaluate, and deploy machine learning models using industry-standard frameworks and tools.
• Develop scalable and production-ready ML solutions that meet enterprise performance, reliability, and governance requirements.
• Implement model monitoring, validation, retraining, and performance optimization strategies.
• Ensure analytical models align with business objectives and deliver measurable value.
• Design and develop end-to-end analytical solutions leveraging Azure Synapse Analytics.
• Build and optimize data pipelines, data transformations, and integrated analytics workflows.
• Collaborate with data engineering teams to ensure efficient data ingestion, preparation, and accessibility.
• Partner with business leaders, product teams, architects, and engineering teams to identify opportunities for data-driven decision-making.
• Translate complex analytical findings into clear business recommendations and actionable insights.
• Communicate effectively with stakeholders across all organizational levels, including senior leadership and C-suite executives.
• Lead discovery workshops, requirements-gathering sessions, and solution design discussions.
• Define analytical approaches, success metrics, and implementation roadmaps aligned with business goals.
• Evaluate solution feasibility, risks, and expected business outcomes.
• Stay current with emerging trends, technologies, and methodologies in Data Science, Artificial Intelligence, and Machine Learning.
• Identify opportunities to improve analytical processes, automation, and model effectiveness.
• Provide technical leadership and guidance to data scientists, analysts, and project teams.
• Review model design, implementation approaches, and analytical outputs to ensure quality and consistency.
• Support capability development through coaching, mentoring, and knowledge sharing.

Senior Associate Data Scientist

• Develop and evaluate LLM-powered solutions such as summarization, classification, extraction and conversational assistants**
• Design prompt strategies, RAG architectures, and fine-tuning/adapter approaches where appropriate**
• Apply NLP and ML methods: text classification, topic modeling, semantic search, embeddings, clustering, regression/causal inference as needed**
• Develop Python-based prototypes and evaluation frameworks for LLM and GenAI use cases**
• Advanced use of object-oriented programming and functional programming patterns for ML workflows

Data Scientist – GenAI Engineer

• Responsible for model governance of data-driven solutions related to IT infrastructure/operations, data ingestion and quality control, and enhancing customer experience.
• Monitor & govern consumer credit models, strategies, and business intelligence solutions through consulting engagements and research.
• Independently validate and review models across their lifecycle to ensure conceptual soundness, data integrity, and regulatory compliance.
• Perform quantitative testing, benchmarking, and back-testing to assess model performance and stability using languages such as R, Python, SQL, Hive, and Spark.
• Identify model limitations, assumptions, and potential risks, and recommend mitigation strategies.
• Prepare comprehensive validation reports and effectively communicate findings to model owners, senior management, and regulatory stakeholders.
• Contribute to strengthening the organization’s model risk governance framework by ensuring adherence to internal policies and industry standards.

MRM Consultant, Data Science & Analytics

• Lead data projects from scoping to delivery, ensuring clear direction, prioritisation, and timely execution
• Work closely with business stakeholders to understand analytical needs and translate them into high-impact data products
• Create robust Power BI reports, dashboards, and data models that deliver actionable insights across the business
• Build, maintain, and optimise end-to-end data pipelines and systems that collect, combine, process, and deliver data
• Own and drive best practice across data architecture, modelling, governance, and documentation
• Act as the internal subject-matter expert for Power BI and data warehousing, providing guidance to the wider organisation
• Proactively identify opportunities to improve data quality, data accessibility, and data-driven decision-making

Data Lead

• Work with large and complex data sets to evaluate, recommend, and support the implementation of business strategies
• Identify and compile data sets using a variety of tools (e.g. SQL, Access)
• Document data requirements, data collection / processing / cleaning, and exploratory data analysis
• May utilize statistical models / algorithms and data visualization techniques
• Assess risk when business decisions are made, ensuring compliance with laws, rules, regulations, and ethical standards.

Data Scientist – Intermediate Analyst

• Work with low to minimum supervision to solve business problems using data and analytics
• Understand and document requirements from various business stakeholders
• Design analytical frameworks to provide insights
• Explore and visualize multiple data sets
• Build end to end data pipelines to handle and process data at scale
• Build machine learning models and/or statistical solutions
• Present results to stakeholders clearly and concisely
• Work collaboratively with team members

• Architect and ship production-grade LLM applications
• Design RAG pipelines that actually work at scale
• Collaborate closely with product and engineering to own problems end-to-end
• Design, build, and deploy AI models, tools, and agents
• Partner with the Head of Data & AI to define the technical architecture
• Implement retrieval augmented generation (RAG) pipelines
• Design and implement Model Context Protocol (MCP) integrations
• Build and maintain MCP servers
• Establish patterns for MCP host/client configuration, access control, and observability
• Implement MLOps and LLMOps practices
• Collaborate with data engineers and platform teams

AI Engineer, Data Scientist

• Lead and oversee the development and execution of data science & AI projects.
• Define the technical roadmap and vision for data science initiatives.
• Provide technical leadership and mentorship to a team of data scientists.
• Collaborate with cross-functional teams to integrate data science solutions into existing systems.
• Design and implement advanced statistical models, algorithms, and machine learning techniques.
• Stay up to date with the latest advancements in data science and guide the team.
• Present complex findings and insights to executive stakeholders.
• Lead the evaluation and adoption of new technologies and methodologies.
• Collaborate with external partners and research institutions.
• Analyse large datasets to uncover trends, patterns, and insights.

Lead Data Scientist

• Stay at the forefront of the field by keeping abreast of the latest advancements in algorithms, tools, and theoretical frameworks.
• Conduct cutting-edge research to identify novel AI/ML methodologies, including Deep Learning (DL), Reinforcement Learning (RL), Generative AI and Agentic AI.
• Perform domain-specific data analysis to identify trends, patterns, and insights relevant to business objectives.
• Collaborate with business stakeholders to understand operational challenges and translate them into analytical tasks.
• Fine-tune and optimize existing AI/ML solutions to reduce bias and improve performance, accuracy in alignment with business goals.
• Ensure that the right architecture and design principles are consistently applied to achieve security, scalability, and long-term sustainability.
• Validate model outputs and ensure they are interpretable, reliable, and actionable for decision-making.

Senior Data Scientist – AI/ML

• Support o9 model build phase covering EDA, Feature Engg, Experimentation, slicing strategy, parameter tuning and reports review in coordination with RDS
• Support resolving model input data defects in coordination with Data Science Leads, BU DP Champions and Data Stewards
• Perform experiments supporting value to o9 model building on o9 experiment zone/tenant
• Participate in o9 Data Science trainings and upskilling plan
• Support Achilles project to harmonize the forecast granularity, driver ingestion, and experimentation
• Support during integration testing and user testing for specific business units in scope
• Support BU wise Data Quality Improvement track
• Build long term sustainable plan for AI/ML Model runs for the specific BU in scope

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