Lead Director, Data Science - Clinical AI

CVS Health•York, PA

5d•$144,200 - $288,400

About The Position

CVS Health is seeking an experienced technical leader in AI and machine learning to join CVS Health's enterprise clinical intelligence platform as a Lead Director, Clinical AI. AI is unlocking new possibilities in healthcare at unprecedented pace, and with that pace comes the responsibility to build capabilities that are clinically rigorous, ethically grounded, and worthy of the trust patients and providers place in them. This role leads the AI strategy and talent behind a growing portfolio focused on maximizing the utility of clinical data to generate evidence-backed insights that power use cases across multiple CVS Health businesses. The successful candidate will set the direction for how the portfolio develops, evaluates, and operates its clinical AI, spanning model development, evaluation rigor, governance and responsible AI, cost efficiency, latency, reliability, and scalability, in close partnership with product and engineering. This leader will partner with product and engineering peers to ensure the portfolio's capabilities fit coherently within the broader clinical intelligence platform and that all use of AI aligns with CVS Health and regulatory policies, procedures, and standards. They will directly manage and develop a team of data scientists staffed across the portfolio's squads, growing the bench of senior technical leaders who will scale with the work. This role sits at the center of building and running production clinical AI at enterprise scale. It is not a research role, and the successful candidate will be energized by the discipline of shipping, operating, and continuously improving software that clinicians and members depend on every day. It requires close collaboration and partnership with product and engineering peers.

Requirements

10+ years of experience in analytics, data science, or applied AI, with hands-on depth in developing and deploying machine learning and deep learning solutions, including modern approaches to natural language processing, large language models, and generative AI.
5+ years setting AI/ML technical direction across multiple teams, coordinating across product, engineering, and business stakeholders.
5+ years of people management experience with a proven track record of hiring, developing, and retaining strong technical teams.
Experience delivering healthcare AI or analytics capabilities to production at enterprise scale, including working with clinical, claims, or member data.
Deep, hands-on experience applying modern production ML discipline, including model evaluation, monitoring, lifecycle management, and managing tradeoffs across cost, latency, reliability, and scalability.
Hands-on experience operating inside healthcare AI regulatory and responsible AI constraints, including HIPAA, NCQA or equivalent quality standards, and emerging AI governance guidance.
Fluent in Python and SQL, with the ability to read, review, and guide the team's technical work at the code and model level.
Strong communication and collaboration skills, including the ability to operate as a peer with senior product and engineering leaders and to represent complex technical strategy and tradeoffs to executive audiences.

Nice To Haves

Experience leading in a squad-based or product operating model, or comparable cross-functional team-of-teams structure.
Deep experience with clinical data from EMR systems, digital chart ingestion pipelines, and unstructured clinical documents such as notes, discharge summaries, and imaging reports.
Experience partnering with clinical informatics, medical directors, screening & diagnosis, quality measurement, or care management stakeholders on AI-driven clinical products.
Experience with AI evaluation and benchmarking frameworks, including approaches for validating clinical AI performance against clinical ground truth and industry benchmarks.

Responsibilities

Set the AI strategy and technical goals for the portfolio in partnership with product and engineering, defining what excellent looks like for clinical AI capabilities in production and how progress against that bar is measured.
Co-shape the technical direction of the portfolio's capabilities in close partnership with product and engineering, resolving cross-discipline tradeoffs at the portfolio level so squads operate against a unified direction.
Ensure the portfolio's clinical AI capabilities fit coherently within the broader clinical intelligence platform, building on shared architectural patterns and data foundations rather than in isolation.
Ensure all use of AI in the portfolio aligns with CVS Health and regulatory policies, procedures, and standards, translating those requirements into concrete engineering and modeling practices the squads adopt.
Advance the portfolio's use of modern AI and machine learning techniques, including large language models and generative AI, to extract structured, evidence-linked insight from clinical data at scale.
Raise the bar on model evaluation, monitoring, and clinical validation so the portfolio ships and operates capabilities that are trustworthy, auditable, and clinically defensible.
Drive continuous improvement in the cost, latency, reliability, and scalability of the portfolio's clinical AI in production, so the platform can support growing demand across businesses and use cases without rework.
Accelerate time-to-market for new clinical AI capabilities by championing reusable model patterns, evaluation harnesses, and generalizable approaches that squads adopt rather than rebuild.
Directly manage and develop a team of data scientists staffed across the portfolio's squads, holding accountability for their performance, growth, and engagement.
Hire, coach, and retain top clinical AI talent, and grow the bench of senior technical leaders who will scale with the portfolio.