Lead Director - Clinical Risk Data Science

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.

Job Description Summary

Leads the Clinical and Risk Modeling Data Science team in developing advanced statistical, mathematical, and machine learning models that transform clinical, claims, and risk data into actionable insights. This role drives the design and deployment of predictive and AI/ML models that improve member health outcomes and inform enterprise clinical and financial strategy.

Job Description

A Brief Overview

Leads a team of data scientists and machine learning engineers responsible for advanced statistical, mathematical, and AI/ML model development supporting clinical analytics and risk modeling. Oversees the end-to-end lifecycle of predictive models — from problem framing and feature engineering through deployment, monitoring, and governance — to drive improved member health, accurate risk capture, and optimized clinical and operational performance. Partners closely with clinical, actuarial, finance, IT, and executive stakeholders to translate complex modeling work into business outcomes.

What you will do

• Leads, develops, and grows a high-performing team of data scientists and machine learning engineers focused on clinical analytics, risk adjustment, and AI/ML model development; sets technical direction, mentors team members, and builds bench strength across statistical modeling, machine learning, and clinical domain expertise.

• Oversees the design, development, and deployment of advanced predictive models, machine learning algorithms, and AI solutions that evaluate clinical scenarios, forecast member risk and outcomes, and identify opportunities to improve quality of care and risk score accuracy.

• Directs data science initiatives leveraging data mining, statistical modeling, natural language processing, and machine learning techniques across clinical, claims, pharmacy, lab, and member-reported data sources.

• Establishes the team's technical standards and best practices for model development, validation, documentation, monitoring, and retraining, ensuring models are explainable, reproducible, and aligned with regulatory and organizational requirements.

• Applies deep expertise in clinical analytics and risk modeling (e.g., HCC/CMS-HCC, condition prevalence, cost and utilization prediction, readmission risk, care gap identification) to deliver models that drive measurable business and clinical impact.

• Develops analytical frameworks, key metrics, and performance dashboards to monitor model performance, clinical quality, risk adjustment outcomes, and operational efficiency.

• Partners with executives, clinicians, actuarial, finance, IT, and business stakeholders to understand priorities, frame analytical problems, and deliver data-driven solutions that inform enterprise strategy.

• Delivers presentations and consultative briefings to senior leadership and cross-functional partners, translating complex modeling concepts and results into clear, actionable recommendations.

• Communicates with leaders to introduce and operationalize incremental KPIs and modeling capabilities that contribute to revenue accuracy, medical cost management, and quality performance targets.

• Establishes and enforces adherence to data governance, model risk management, privacy, and compliance policies, and educates team members on data science and modeling best practices.

Minimum Requirements

• 10 years of progressive experience in data science, advanced analytics, or statistical modeling, with significant experience in healthcare, health plan, or clinical domains.

• Demonstrated experience leading and developing teams of data scientists or machine learning engineers.

• Deep technical expertise in statistical modeling, machine learning, and AI techniques, with hands-on experience deploying models into production environments.

• Adept at execution and delivery (planning, delivering, and supporting) skills.

• Adept at business intelligence and translating analytical work into business outcomes.