Lead Data Scientist - Forecasting
Hispanic Alliance for Career Enhancement, Olympia, WA, United States
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 builds scalable forecasting systems that support pricing, promotions, and assortment decisions across the retail business. As a Lead Data Scientist, you will own how demand is modeled and used for decision‑making, not just how it is predicted.
This role focuses on defining and scaling a unified forecasting framework that separates baseline demand, incremental lift, produces consistent outputs across scenarios and decisions, and scales across categories, offers, and new use cases. You will work across data science, engineering, and business teams to ensure forecasts are not just accurate, but stable and usable in real decision workflows.
Responsibilities
Own the design and evolution of a unified forecasting architecture, defining how demand is constructed (baseline, incremental, total) and ensuring consistent, scalable behaviour across decision systems
Work across forecasting, econometrics, and optimization decision layers to design and validate end-to-end forecasting systems
Evaluate tradeoffs across forecasting approaches (e.g., ARIMA, Prophet, gradient boosting, LSTMs, TFT, hybrid models) and ensure outputs are stable, interpretable, and decision‑ready
Define and enforce standards for incremental consistency, attribution accuracy, and reconciliation across hierarchy levels (SKU, category, chain)
Translate forecasting outputs into decision frameworks (planning, allocation, simulation) that are usable and reliable for the business
Develop scenario planning and simulation frameworks to measure the business impact of pricing, promotions, and assortment decisions
Monitor model performance, perform backtesting, and improve both accuracy and stability over time
Implement robust MLOps practices for deployment, monitoring, and retraining in cloud environments (Azure, GCP, AWS)
Integrate internal and external data sources (e.g., coupon redemption, merchandising, competitive, macroeconomic) into scalable forecasting pipelines
Partner with data engineering to build high-quality, production-ready data pipelines
Coach and mentor junior data scientists, setting standards for forecasting and applied analytics
Required Qualifications
7+ years of experience in forecasting or demand modeling, including 3+ years owning end-to-end demand construction (baseline, incremental, total) in production systems
3+ years of experience defining or evolving unified forecasting architectures, ensuring consistency across use cases (pricing, promotions, assortment)
4+ years of hands‑on experience building forecasting models across multiple paradigms (e.g., statistical + ML/deep learning), with experience handling hierarchical forecasting and reconciliation across at least 2 levels (e.g., SKU, category, store, chain)
3+ years of experience evaluating model performance beyond accuracy, including stability, consistency of incremental effects, and behavior across scenarios
Proven track record of deploying at least 2 production forecasting systems with measurable impact, including ≥10% improvement in accuracy (MAPE/wMAPE/sMAPE) or equivalent business KPIs
Experience delivering at least 2+ enterprise‑level data products in cross‑functional environments (engineering, merchandising, pricing, promotions, assortment), from design to production
3+ years of experience with MLOps practices and cloud platforms (e.g., Azure, AWS, or GCP), including at least 2 tools such as Git‑based workflows, Docker, Kubernetes, Kubeflow, CI/CD pipelines, Databricks, Spark
4+ years of experience with Python and SQL for large‑scale data processing
Preferred Qualifications
Experience applying forecasting to pricing, promotions, or assortment decisions, including modeling demand drivers and interpreting impact on business outcomes
Experience building or using scenario planning and simulation frameworks where forecast outputs directly drive planning, allocation, or optimization decisions
Familiarity with causal inference, elasticity modeling, or demand decomposition approaches used to separate baseline demand and incremental effects
Experience with end-to-end forecast lifecycle management (versioning, retraining, data drift monitoring)
Experience mentoring junior team members and setting standards for forecasting approaches, model validation, and code quality
Strong ability to communicate model behavior, tradeoffs, and decision implications clearly to senior leadership and business stakeholders
Experience applying generative AI (e.g., embeddings, LLMs, foundation models) to forecasting, feature engineering, or automation
Education
Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field
Advanced degree (Master's or PhD) preferred in quantitative disciplines (e.g., Econometrics, Operations Research, Machine Learning) with applied experience in timeseries forecasting, demand modeling, or causal inference
Pay Range
$130,295.00 - $260,590.00
Great Benefits for Great People
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.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
#J-18808-Ljbffr
Position Summary
The Forecasting Center of Excellence (COE) at CVS Health builds scalable forecasting systems that support pricing, promotions, and assortment decisions across the retail business. As a Lead Data Scientist, you will own how demand is modeled and used for decision‑making, not just how it is predicted.
This role focuses on defining and scaling a unified forecasting framework that separates baseline demand, incremental lift, produces consistent outputs across scenarios and decisions, and scales across categories, offers, and new use cases. You will work across data science, engineering, and business teams to ensure forecasts are not just accurate, but stable and usable in real decision workflows.
Responsibilities
Own the design and evolution of a unified forecasting architecture, defining how demand is constructed (baseline, incremental, total) and ensuring consistent, scalable behaviour across decision systems
Work across forecasting, econometrics, and optimization decision layers to design and validate end-to-end forecasting systems
Evaluate tradeoffs across forecasting approaches (e.g., ARIMA, Prophet, gradient boosting, LSTMs, TFT, hybrid models) and ensure outputs are stable, interpretable, and decision‑ready
Define and enforce standards for incremental consistency, attribution accuracy, and reconciliation across hierarchy levels (SKU, category, chain)
Translate forecasting outputs into decision frameworks (planning, allocation, simulation) that are usable and reliable for the business
Develop scenario planning and simulation frameworks to measure the business impact of pricing, promotions, and assortment decisions
Monitor model performance, perform backtesting, and improve both accuracy and stability over time
Implement robust MLOps practices for deployment, monitoring, and retraining in cloud environments (Azure, GCP, AWS)
Integrate internal and external data sources (e.g., coupon redemption, merchandising, competitive, macroeconomic) into scalable forecasting pipelines
Partner with data engineering to build high-quality, production-ready data pipelines
Coach and mentor junior data scientists, setting standards for forecasting and applied analytics
Required Qualifications
7+ years of experience in forecasting or demand modeling, including 3+ years owning end-to-end demand construction (baseline, incremental, total) in production systems
3+ years of experience defining or evolving unified forecasting architectures, ensuring consistency across use cases (pricing, promotions, assortment)
4+ years of hands‑on experience building forecasting models across multiple paradigms (e.g., statistical + ML/deep learning), with experience handling hierarchical forecasting and reconciliation across at least 2 levels (e.g., SKU, category, store, chain)
3+ years of experience evaluating model performance beyond accuracy, including stability, consistency of incremental effects, and behavior across scenarios
Proven track record of deploying at least 2 production forecasting systems with measurable impact, including ≥10% improvement in accuracy (MAPE/wMAPE/sMAPE) or equivalent business KPIs
Experience delivering at least 2+ enterprise‑level data products in cross‑functional environments (engineering, merchandising, pricing, promotions, assortment), from design to production
3+ years of experience with MLOps practices and cloud platforms (e.g., Azure, AWS, or GCP), including at least 2 tools such as Git‑based workflows, Docker, Kubernetes, Kubeflow, CI/CD pipelines, Databricks, Spark
4+ years of experience with Python and SQL for large‑scale data processing
Preferred Qualifications
Experience applying forecasting to pricing, promotions, or assortment decisions, including modeling demand drivers and interpreting impact on business outcomes
Experience building or using scenario planning and simulation frameworks where forecast outputs directly drive planning, allocation, or optimization decisions
Familiarity with causal inference, elasticity modeling, or demand decomposition approaches used to separate baseline demand and incremental effects
Experience with end-to-end forecast lifecycle management (versioning, retraining, data drift monitoring)
Experience mentoring junior team members and setting standards for forecasting approaches, model validation, and code quality
Strong ability to communicate model behavior, tradeoffs, and decision implications clearly to senior leadership and business stakeholders
Experience applying generative AI (e.g., embeddings, LLMs, foundation models) to forecasting, feature engineering, or automation
Education
Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field
Advanced degree (Master's or PhD) preferred in quantitative disciplines (e.g., Econometrics, Operations Research, Machine Learning) with applied experience in timeseries forecasting, demand modeling, or causal inference
Pay Range
$130,295.00 - $260,590.00
Great Benefits for Great People
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.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
#J-18808-Ljbffr