• Partner with the AI Technical Product Manager to support data-driven decision-making across AI initiatives • Analyze datasets to assess the potential functional and business impact of proposed features • Define success metrics and measure post-implementation performance to evaluate actual impact • Provide actionable insights that inform product roadmap, prioritization, and ROI • Collaborate with ML Engineers to identify machine learning opportunities using proprietary datasets • Design and execute experiments (A/B testing, hypothesis testing) to validate product and AI feature effectiveness • Build dashboards, reports, and visualizations to communicate insights to technical and non-technical stakeholders • Ensure data quality, integrity, and proper usage across projects.

• Identify valuable data sources and automate collection processes • Undertake preprocessing of structured and unstructured data • Analyze large amounts of information to discover trends and patterns • Build predictive models and machine-learning algorithms • Combine models through ensemble modeling • Present information using data visualization techniques • Propose solutions and strategies to business challenges • Collaborate with engineering and product development teams.

• Build, optimize, and deploy scalable forecasting models that support pricing, promotions, and assortment strategies across multiple product categories • Apply advanced statistical, machine learning, and deep learning methods (e.g., ARIMA, Prophet, gradient boosting, LSTMs, hybrid ensembles) for forecasting at SKU, category, and chain levels • Implement robust MLOps practices for model deployment, monitoring, and retraining using cloud platforms (Azure, GCP, AWS) • Integrate multiple internal and external data sources (e.g., coupon redemption, merchandising, competitive, and macroeconomic data) into forecasting pipelines • Collaborate with data engineering to ensure scalable, high-quality data pipelines • Partner with business stakeholders in pricing, promotions, and assortment to design and validate forecast-driven decision workflows • Coach and mentor junior data scientists, sharing best practices in forecasting, MLOps, and applied analytics • Monitor forecast accuracy, perform backtesting, and refine models to reduce error rates and improve stability • Develop frameworks for scenario planning and simulation to measure business impact of promotions, pricing strategies, and assortment changes

• Solve complex problems, leverage large datasets, and build scalable and deployment ready models that work well with CVS Retail’s diverse product portfolio. • Guide the modeling approach, design and deploy end-to end solutions utilizing Operations Research (OR), econometric modeling, and forecasting along with the functional team leads • Partner with the Data Engineering team to architect scalable and fail-proof production pipelines, define the unified technical vision, and set standards • Stay up to date on the state-of-the-art modeling methodologies, initiate, lead, and inspire Research and Development efforts, define long-term technical vision and strategic roadmap • Act as a consultant who translates complex requirements and data science findings into decision ready narratives for cross-functional teams • Mandate and champion rigorous performance validation • Provide advanced technical mentorship, proactively identify critical organizational skill gaps, and standardize hiring processes.