Lead Data Scientist – Forecasting

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At CVS Health, we’re building a world of health around every consumer and surrounding ourselves with dedicated colleagues who are passionate about transforming health care.

As the nation’s leading health solutions company, we reach millions of Americans through our local presence, digital channels and more than 300,000 purpose-driven colleagues – caring for people where, when and how they choose in a way that is uniquely more connected, more convenient and more compassionate. And we do it all with heart, each and every day.

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.

• 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

• 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., Git Hub/Git Lab, 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

• 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…