Principal Product Manager, Biology

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company’s RAS(ON) inhibitors daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, are currently in clinical development. As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway. The Opportunity: We are seeking a Principal Product Manager, Biology to shape products and capabilities that help Biology and Discovery teams design, execute, analyze, and learn from experiments faster, with trusted data and AI-enabled support. This role will define and deliver product strategy for Biology workflows, data products, and AI-enabled decision support on RevCore, our enterprise Data, Digital, and AI platform. The mandate is to improve experiment traceability, reduce manual data preparation, accelerate cross-study analysis, and make Biology insights easier to generate and act on. You will partner with scientists across Protein Science, Structural Biology, Screening Sciences, Sample Management, In Vivo Research, Pathology, Translational Research, Computational Biology, Data Science, ML Engineering, Data Engineering, IT, and platform teams to turn complex research workflows into intuitive, scalable products. Product surfaces may include experiment planning workflows, assay and screening result review, sample and reagent lineage, cross-study analysis, and “Ask your Biology data” experiences. Own Biology product strategy and measurable outcomes Define the vision and roadmap for Biology products and capabilities across Protein Science, Structural Biology, Screening Sciences, In Vivo Research, Pathology, Translational Research, and related discovery workflows. Build a Now, Next, Later roadmap from foundational Biology data products to self-service analytics, workflow applications, and AI-enabled decision support. Set success metrics tied to experiment traceability, data capture quality, data preparation time, result interpretation cycle time, scientific adoption, and program decision support. Prioritize capabilities that reduce manual scientific workflows, improve data reuse, increase confidence in results, and scale across programs and research teams. Shape solutions around Biology workflows and decisions Understand workflows for wet-lab scientists, protein scientists, structural biologists, screening scientists, in vivo scientists, pathologists, translational scientists, computational biologists, and program teams. Design solutions around key decision moments such as construct selection, assay design and interpretation, screening cascade analysis, hit or lead characterization, in vivo study review, pathology readouts, cross-study comparison, translational insights, and program prioritization. Translate Biology workflows into clear product requirements, user stories, evaluation criteria, and prioritized capabilities. Determine when to build, buy, partner, or integrate based on user value, scientific need, tool maturity, scalability, interoperability, and maintainability. Establish reusable Biology data capabilities Partner with technical teams, scientific system owners, vendors, and platform teams to deliver priority Biology capabilities across RevCore and core research platforms. Clarify systems of record and reusable data products for key Biology data, including samples, reagents, constructs, assay results, screening data, structures, methods, study results, imaging, pathology readouts, and translational datasets. Improve data quality at the point of capture across ELN, LIMS, assay and screening systems, imaging, pathology, workflow, and analysis platforms through better metadata, QC, annotation, and usability patterns. Ensure Biology capabilities turn scientific, experimental, imaging, translational, and computational data into decision-grade insights, not just searchable records or dashboards. Enable self-service discovery, AI use cases, and adoption Enable self-service access, search, semantic discovery, cross-study analysis, and “Ask your Biology 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 scientific copilots, experiment summarization, automated annotation, assay interpretation support, screening insights, cross-study analysis, and workflow automation. Drive rollout and continuous improvement through usage metrics, feedback loops, training, and measurable workflow improvements. Required Skills, Experience and Education: 10 years of experience in Product Management, Data Product Management, Research Informatics, Scientific Data Platforms, Bioinformatics, Computational Biology, or related roles within biotech, pharma, life sciences, or another research-intensive environment. Strong product leadership experience defining vision, shaping strategy, building roadmaps, prioritizing tradeoffs, and delivering measurable outcomes. Deep understanding of Biology research workflows across domains such as Protein Science, Structural Biology, Screening Sciences, In Vivo Research, Pathology, Translational Research, or related discovery functions. Experience translating scientific workflows into scalable product capabilities, user stories, evaluation criteria, and product requirements. Working knowledge of Biology data and systems, including experimental metadata, assay and screening data, sample and reagent data, structural, imaging, in vivo, pathology, translational, ELN/LIMS, and downstream analysis workflows. Technical fluency across data platforms, data integration, analytics, data quality, governance, metadata, ontologies, and interoperability practices. Experience with research systems such as ELN, LIMS, assay platforms, screening systems, imaging systems, pathology systems, scientific workflow tools, analysis platforms, and related informatics systems. Strong communication and stakeholder management skills across scientific, computational, technical, vendor, and business teams. D., M.S., B.S., or equivalent experience in Life Sciences, Biology, Bioinformatics, Computational Biology, Computer Science, Engineering, Information Systems, or a related field. Preferred Skills: Experience evaluating, implementing, or integrating SaaS platforms, scientific workflow tools, screening platforms, imaging/pathology platforms, analysis platforms, or vendor solutions for Biology research use cases. Experience enabling scientific data foundations for advanced analytics, machine learning, GenAI, scientific copilots, knowledge graphs, or decision-support products. Experience building self-service data access, search, semantic discovery, cross-study analysis, natural language query, or “Ask your data” experiences for scientific users. 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. Familiarity with FAIR data principles, scientific ontologies, metadata standards, knowledge management, or scientific data interoperability approaches. Comfort operating in an emerging biotech environment where strategy, execution, ambiguity, evolving scientific needs, vendor complexity, and hands-on problem solving are part of the work. #LI-Hybrid #LI-YG1 The base pay salary range for this full-time position for candidates working onsite at our headquarters in Redwood City, CA is listed below. The range displayed on each job posting is intended to be the base pay salary range for an individual working onsite in Redwood City and will be adjusted for the local market a candidate is based in. Our base pay salary ranges are determined by role, level, and location. Individual base pay salary is determined by multiple factors, including job-related skills, experience, market dynamics, and relevant education or training. Please note that base pay salary range is one part of the overall total rewards program at RevMed, which includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities. Revolution Medicines is an equal opportunity employer and prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, disability, marital status, medical condition, and veteran status. Revolution Medicines takes protection and security of personal data very seriously and respects your right to privacy while using our website and when contacting us by email or phone. We will only collect, process and use any personal data that you provide to us in accordance with our CCPA Notice and Privacy Policy . For additional information, please contact privacy@revmed.com . Base Pay Salary Range $211,000 — $264,000 USD We are aware of recent recruitment scams in which individuals or organizations falsely represent themselves as being affiliated with Revolution Medicines. These scams may appear as false job advertisements or unsolicited contacts through communication or chat platforms, email, phone, or text message. Please note that Revolution Medicines does not extend unsolicited employment offers and will never ask candidates to provide financial information, purchase equipment, or pay fees as part of the hiring process. All legitimate communication from Revolution Medicines will come from an official @revmed.com email address. If you believe you’ve been contacted by someone impersonating a Revolution Medicines recruiter, please report it to careers@revmed.com so we can share these impersonations with our IT team for tracking and awareness.