Director Product Management – AI / ML Platform

CVS Health•Washington, NV

1d•$100,000 - $231,540

About The Position

CVS Health’s Analytics & Behavior Change (A&BC) is an organization working to solve some of the most challenging problems at the intersection of technology and healthcare. A&BC leverages advanced analytics, machine learning, modeling, and hypothesis-driven approaches to transform data into actionable, customer-centric insights driving growth, health outcome improvements, and access to health care across all our businesses in CVS Health. Our teams build next generation data and machine learning platforms and software products that help power CVS Health to make healthier happen for 100+ million customers. The A&BC organization is looking to grow its Product team supporting clinical decision support intelligence. Join us as we embark on an exciting journey to drive a transformational shift in how CVS Health leverages technology and analytics to become the leader in consumer healthcare in the U.S. As a Director within A&BC, you will be responsible for leading the creation, launch, and maintenance of an AI-powered, clinical data insights platform that powers both automation and workflow tools for Aetna Clinicians. This role requires a strong understanding of product management, clinical data, AI-powered insights acceleration, and cross-team collaboration. The ideal candidate has familiarity with the following areas: Insight generation from unstructured clinical documentation using genAI, natural language processing and traditional ML, as well as experience building platform products. Reusable services designed to serve multiple downstream applications rather than a single use case. Working effectively with cross-functional teams, most importantly data scientists and machine learning engineers, to bring clinical data and AI products from inception to production. This is a hands-on, individual-contributor role today. You'll help define how the platform team should be structured as scope and complexity grow, with the opportunity to build and lead that team over time.

Requirements

8+ years of product management experience building enterprise software, platforms, or AI/ML and Generative AI products, including experience taking complex technical products from concept through launch, adoption, and iteration.
8+ years demonstrating exceptional analytical and product judgment, with ability to structure ambiguous problems and translate quantitative and qualitative evidence into strategy and measurable outcomes.
6+ years’ experience working with AI/ML and Generative AI products and application architectures, with technical fluency to partner deeply with Data Science and Engineering.
5+ years of experience influencing senior stakeholders and driving decisions across complex, cross-functional, matrixed organizations.
5+ years of experience demonstrating executive communication and storytelling skills, with the ability to translate fluidly between technical, clinical, and business audiences.
Bachelor's degree required, preferably in a quantitative, analytical, or engineering field.

Nice To Haves

Domain expertise in healthcare or health insurance, including familiarity working with unstructured clinical documentation.
Experience building platform products and reusable services designed to serve multiple downstream applications rather than a single use case.
Experience with healthcare data interoperability standards and multi-channel data intake (e.g., fax/EDI, EMR feeds).
Experience evaluating emerging AI/ML technologies and external vendor solutions.
Experience defining and measuring platform adoption and business outcomes.

Responsibilities

Develop a deep understanding of clinical and application-team needs through direct discovery, product data, and quantitative/qualitative analysis, to determine what should be built once and reused vs. left application-specific.
Structure ambiguous problems, form and test hypotheses, and prioritize the platform investments with the highest clinical and business value.
Define product strategy and own the platform roadmap, translating recurring needs from application/activation teams into reusable, scalable platform capabilities.
Own multi-year roadmap and investment priorities, making evidence-based trade-offs across clinical value, team productivity, technical feasibility, reliability, and business outcomes.
Lead the platform from concept through delivery in close partnership with Data Science and Engineering, making clear technical and product trade-offs (build vs. integrate vs. vendor).
Partner with application teams to launch and scale platform capabilities, accelerate adoption, and identify barriers to reuse.
Define platform health and success metrics: Coverage, accuracy, reliability, adoption.
Using data and experimentation to inform roadmap and investment decisions.
Influence senior stakeholders and align Data Science, Engineering, and cross-functional partners around platform strategy, priorities, and trade-offs.