Sr Product Manager - Healthcare AI

McKesson•Columbus, OH

4d•$119,300 - $198,800

About The Position

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. CoverMyMeds’ Shared Capabilities and Partnerships product team is looking for a Senior Product Manager to lead our Next Best Action (NBA) initiative — someone who is genuinely energized by data, AI, and machine learning, and who knows how to turn that curiosity into a recommendation and decisioning capability that drives real outcomes across the care journey. This role requires the ability to translate ambiguous, data-rich problems into a clear product strategy, and to build the trust needed to align stakeholders across a large, matrixed organization. About the Role The Senior Product Manager will own the Next Best Action initiative end to end: defining what “best” means for the users and workflows we serve, partnering with Data Science and Data Engineering to build the decisioning capability that gets there, and continuously improving it through experimentation. You’ll operate at the intersection of product strategy and applied AI/ML — not writing the models yourself, but framing the problems, prioritizing the signals, and holding the bar on measurement and responsible use.

Requirements

Degree or equivalent experience. Typically requires 7+ years of relevant experience
Healthcare Product Management experience or familiarity with technical product development methodologies (e.g., Agile)
Demonstrated fluency in data, AI, and machine learning concepts — enough to partner credibly with Data Science and ML Engineering on problem framing, model trade-offs, and evaluation metrics in a Healthcare setting
Experience defining and using experimentation (A/B testing) and quantitative measurement to guide product decisions
Experience working in cross-functional product teams spanning Engineering, Data Science, UX, Operations, and Commercial partners
Technical competency with direct experience in data and analytics, data science, or AI/ML, enough to partner credibly with Data Science and ML Engineering on problem framing, model trade-offs, and evaluation metrics
Healthcare experience, preferably Biopharma and/or Payer expertise

Nice To Haves

Prior experience building or launching a recommendation engine, decisioning system, or personalization capability
Familiarity with responsible AI principles (fairness, explainability, bias mitigation) and how they apply in a regulated healthcare context
Experience with healthcare technology, patient support programs, enrollment workflows, or HCP-facing products
Strong understanding of product-market dynamics, customer needs, and competitive landscapes
Financial acumen, including experience connecting product decisions to P&L outcomes and business goals
Proven stakeholder engagement and influence skills in matrixed environments
Ability to prioritize effectively across competing initiatives and drive alignment toward execution
Comfort operating in ambiguity with a bias toward action and continuous improvement

Responsibilities

Drive product strategy for Next Best Action, grounding decisions in data, discovery, and research
Define which signals, use cases, and workflows the recommendation/decisioning engine should prioritize, and translate business and customer goals into a roadmap for the model and product experience
Partner closely with Data Science and ML Engineering to shape problem framing, success criteria, and trade-offs — bringing product judgment to model development without owning the modeling itself
Own the experimentation and measurement strategy for recommendations: define what “good” looks like, design A/B tests, and build feedback loops that continuously improve recommendation quality
Partner with Data Engineering on the data foundations — quality, access, and integration — that the recommendation engine depends on
Champion responsible AI practices, ensuring recommendations are fair, explainable, and compliant with healthcare regulatory and privacy requirements, in partnership with Legal, Privacy and Compliance
Navigate complexity across a large, matrixed organization — aligning stakeholders, clarifying tradeoffs, and building the trust needed to move work forward
Influence and align cross-functional partners across Engineering, Data Science, UX, Commercial, and Operations to gain buy-in on product direction and priorities
Own and communicate roadmap priorities to leadership, ensuring transparency on progress, tradeoffs, and decisions at every phase
Apply design thinking methodologies to solve complex problems, iterate on solutions, and drive MVP delivery and customer co-innovation opportunities