Senior Technical Product Manager – AI / ML

CVS Health•Washington, NV

6d•$106,605 - $284,280

About The Position

CVS Health’s Analytics & Behavior Change (A&BC) organization tackles complex challenges at the intersection of healthcare and technology. We use advanced analytics, machine learning, modeling, and hypothesis‑driven approaches to turn data into actionable insights that drive growth, improve outcomes, and increase access to care across 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 advanced analytic automation and smart routing. 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 Senior Technical Product Manager within A&BC, you will be responsible for contributing to the creation, launch, and maintenance of AI-powered analytics support Aetna clinicians. This role requires a strong understanding of Product Management, Machine Learning algorithms, AI-powered insights acceleration, and cross-team collaboration. The ideal candidate has strong data literacy, experience with MLOps, threshold tuning & statistical risk management and experience with clinical policy and workflow integration. This person can work effectively with cross-functional teams, most importantly data scientists and machine learning engineers, to bring clinical data and AI/ML products from inception to production.

Requirements

5+ years of overall work experience.
3+ years’ experience working cross-functionally with Machine Learning Engineering and Data Science.
2 years’ Product Management, Product Management-adjacent, or Consulting experience (closely partnering with Data Scientists and Engineers).
2+ years’ experience working with AI, data analytics, data products, and/or complex analytics utilizing clinical or claims data to accelerate insights and drive value.
2+ years demonstrating the ability operate effectively within a complex stakeholder environment, driving consensus and fostering collaborative decision-making processes.
2+ years demonstrating the ability to communicate (oral and written), with experience in communicating technical concepts and implications to business stakeholders, including clinicians, as well as business concepts and requirements to Data Science, Engineering, and other technical teams.
2+ years of experience supporting and managing products throughout the full lifecycle, with a proven track record of contributing to successful product delivery and a strong understanding of QA methodologies and best practices.
2+ years of experience demonstrating the ability to drive results in complex, fast-paced environments, effectively navigating ambiguity, influencing stakeholders, and delivering business outcomes.
Bachelor’s degree in Computer Science, Engineering, Data Science, Analytics, Mathematics, Physics, Applied Sciences, or a related field required.

Nice To Haves

Familiarity with MLOps, including model monitoring, data/concept drift detection and threshold calibration for predictive decision engines
Familiarity with healthcare data interoperability standards
Deep knowledge of various types of healthcare data, including experience utilizing clinical and claims data to create a longitudinal patient view or using AI to drive clinical data insights.
Strong interest in the Healthcare sector; the ideal candidate should be passionate about making healthcare more efficient and want to be part of a transformational change in the Healthcare industry.
Technical product management experience and/or experience with general AI/machine learning teams.
Master’s degree preferred.

Responsibilities

Create product requirements documents, acceptance criteria, and detailed product specifications to communicate feature requirements to Data Science, Engineering, and Leadership teams.
Support Engineering and Data Science teams to implement features by clarifying requirements, facilitating technical discussions, and helping technical team members to understand the end users and the business problem being solved. Ensure end-to-end insight and collaboration.
Facilitate problem solving and prioritization across teams, factoring in trade-offs between speed, scalability, accuracy, and quality. Be prepared to get into technical details during discovery and while supporting the team to make trade-off decisions.
Facilitate cross-team hand-offs and define data SLAs and interface contracts with upstream and downstream teams.
Develop go-live timelines with your core data science team and engineering partners; align timelines and scope with multiple developer teams and stakeholders.
Work closely with clinicians and operations to confirm business value, inform problem statements, and quantify the value of your team’s potential solution.
Liaise with clinicians to validate model results and to facilitate review and refinement.
Define, track, and analyze product performance metrics to drive feature prioritization and demonstrate ROI.
Work with internal customers and executive stakeholders to maintain product roadmaps.
Follow Agile best practices, including directly leading and guiding a dedicated scrum team of data scientists and software engineers.