Principal Product Manager, Real World Evidence Intelligence

Revolution Medicines•Redwood City, CA

8d•$186,000 - $233,000•Hybrid

About The Position

Revolution Medicines is seeking a Principal Product Manager, Real-World Evidence Intelligence to shape products and capabilities that help teams use real-world data to design better studies, evaluate feasibility, support regulatory and evidence strategies, communicate value, and monitor outcomes across the product lifecycle. This role will define and deliver RWE Intelligence capabilities through the right mix of internal products, RevCore platform capabilities, external data assets, SaaS platforms, vendor partnerships, and integrations. The mandate is to improve study design, protocol feasibility, enrollment assumptions, external comparator readiness, evidence generation speed, value communication, and post-approval monitoring. You will work on high-impact capabilities where data fitness, transparency, reproducibility, and speed directly affect clinical development decisions, regulatory confidence, access strategy, and patient impact.

Requirements

12+ years of experience in Product Management, Data Product Management, RWE Informatics, Life Sciences Data Platforms, Evidence Strategy, Epidemiology, HEOR Analytics, Outcomes Research, or related roles within biotech, pharma, healthcare technology, consulting, data providers, or another regulated environment.
Strong product leadership experience defining vision, shaping strategy, building roadmaps, prioritizing tradeoffs, influencing senior stakeholders, and delivering measurable outcomes.
Deep understanding of how RWE supports clinical development, regulatory strategy, medical strategy, HEOR, market access, safety, and post-approval evidence generation.
Experience translating evidence workflows into scalable product capabilities, user stories, evaluation criteria, and product requirements.
Working knowledge of RWD sources, observational methods, and evidence-grade practices, including claims, EHR, registries, labs, genomics, PROs, cohort definition, confounding, endpoint construction, comparative effectiveness, external comparators, synthetic cohorts, data quality, provenance, reproducibility, privacy, governance, and compliance.
Product judgment to evaluate build, buy, partner, and integration options based on user value, data fitness, vendor maturity, compliance needs, scalability, interoperability, and maintainability.
Strong communication and stakeholder management skills across evidence, clinical, medical, market access, safety, technical, legal, compliance, vendor, and executive audiences.
Advanced degree or equivalent experience in Life Sciences, Public Health, Epidemiology, Health Economics, Outcomes Research, Biostatistics, Biomedical Informatics, Computer Science, Information Systems, Engineering, or a related field.

Nice To Haves

Experience delivering RWE data products, evidence intelligence platforms, cohort discovery products, HEOR analytics platforms, or AI-enabled evidence decision-support capabilities.
Experience with oncology, precision medicine, biomarker-driven populations, external control arms, regulatory-grade RWE, or post-approval evidence generation.
Experience evaluating, implementing, or integrating SaaS platforms, RWD vendors, evidence platforms, data marketplaces, analytics partners, or external data providers.
Experience using modern AI, analytics, workflow, and low-code tools to prototype product concepts, validate user needs, and de-risk ideas before larger product, platform, or vendor investments.
Experience enabling self-service data access, cohort discovery, semantic search, natural language query, evidence landscaping, or “Ask your data” experiences.
Comfort operating in an emerging biotech environment where strategy, execution, ambiguity, privacy, compliance, vendor complexity, and hands-on problem solving all matter.

Responsibilities

Own RWE Intelligence strategy and measurable outcomes
Define the vision and roadmap for RWE products and capabilities across clinical development, regulatory, medical, HEOR, market access, safety, and post-approval use cases.
Set success metrics tied to cohort discovery speed, question-to-answer time, data readiness, evidence generation cycle time, external comparator feasibility, value evidence readiness, and adoption.
Prioritize capabilities that reduce manual evidence workflows, improve decision quality, support evidence strategy, and scale across programs, studies, and indications.
Shape solutions around evidence questions and decisions
Design product and platform solutions around key decision moments such as eligibility criteria evaluation, protocol feasibility, enrollment assumptions, RWD source selection, cohort definition, endpoint selection, external comparator feasibility, payer evidence planning, and post-approval monitoring.
Conduct product discovery with evidence teams to identify high-value questions, workflow friction, reusable data needs, and scalable solution opportunities.
Translate evidence workflows into clear requirements, user stories, evaluation criteria, and prioritized capabilities.
Determine when to build, buy, partner, or integrate based on user value, data fitness, vendor maturity, compliance needs, scalability, interoperability, and maintainability.
Establish trusted, reusable RWE data capabilities
Partner with technical teams, vendors, and data providers to deliver priority RWE capabilities across RevCore and core evidence platforms.
Clarify trusted sources and reusable data products across claims, EHR, registries, labs, genomics, mortality, SDOH, patient-reported outcomes, treatment patterns, utilization, cost, outcomes, survival, and safety data.
Define evidence-grade standards for fitness-for-purpose, provenance, cohort reproducibility, analytic transparency, data quality, privacy, governance, and auditability.
Ensure RWE capabilities turn real-world, clinical, economic, safety, regulatory, and vendor data into decision-grade insights, not just dashboards or one-off analyses.
Enable self-service evidence intelligence, AI use cases, and adoption
Enable self-service cohort discovery, evidence landscaping, outcomes exploration, and “Ask your RWE data” experiences across priority datasets and platforms.
Use modern AI, analytics, workflow, and low-code tools to prototype concepts, validate user needs, and de-risk ideas before larger product, platform, or vendor investments.
Partner with Data Science and ML Engineering to identify AI and GenAI use cases such as cohort discovery copilots, evidence synthesis, study design support, endpoint mapping, cohort attrition explanation, payer evidence summaries, and insight generation.
Drive rollout and continuous improvement through usage metrics, feedback loops, training, and measurable workflow improvements.