Lead Data Scientist – Forecasting

CVS Health • Washington, DC

4d

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary The Forecasting Center of Excellence (COE) at CVS Health develops scalable forecasting solutions that power smarter pricing, promotions, and assortment decisions across the retail business. As a Lead Data Scientist, you will play a key role in advancing forecasting models, deploying production-ready pipelines, and guiding junior team members. This role requires strong technical expertise in time-series modeling, machine learning, and MLOps practices, along with hands-on ability to design, implement, and scale models. You will also collaborate closely with data engineering, merchandising, pricing, promotions, and assortment teams to integrate diverse datasets (including coupon and external data) and translate modeling insights into measurable business impact. In this role, you will have the opportunity to: 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

Requirements

• 7+ years of experience in data science, forecasting, or applied predictive modeling
• 4+ years of experience building and deploying time-series forecasting models using methods such as ARIMA, Prophet, gradient boosting, LSTMs, or hybrid ensembles
• 4+ years of experience with Python and SQL for large-scale data processing
• 3+ years of experience with MLOps tools and practices (e.g., GitHub/GitLab, Docker, Kubernetes, Kubeflow, CI/CD pipelines)
• 3+ years of experience using cloud platforms (Azure, AWS, or GCP) and distributed computing frameworks (e.g., Databricks, Spark)
• Proven track record of deploying at least 2 production forecasting models that delivered ≥10% improvement in accuracy (e.g., reduction in MAPE, WMAPE, or sMAPE
• Experience working with cross-functional teams (engineering, merchandising, pricing, assortment) to deliver at least 2+ enterprise-level data-driven solutions from design to production

Nice To Haves

• Experience forecasting across multiple hierarchy levels (SKU, category, store, chain) and handling temporal aggregation challenges
• Exposure to promotion, pricing, and assortment data as forecast drivers
• Familiarity with simulation frameworks for what-if analysis and demand scenario planning
• Experience applying generative AI (e.g., embeddings, LLMs, foundation models) to forecasting, feature engineering, or automation
• Strong ability to communicate technical results to senior leadership and business stakeholders
• Experience mentoring junior team members, setting standards for modeling practices, and guiding code reviews
• Experience with end-to-end forecast lifecycle management (versioning, retraining, data drift monitoring)

Responsibilities

• 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

Benefits

• Affordable medical plan options, a 401(k) plan (including matching company contributions), and an employee stock purchase plan.
• No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching.
• Benefit solutions that address the different needs and preferences of our colleagues including paid time off, flexible work schedules, family leave, dependent care resources, colleague assistance programs, tuition assistance, retiree medical access and many other benefits depending on eligibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level