Vice President, Head of Data Product Management

Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) Inhibitors designed to suppress diverse oncogenic variants of RAS proteins, and RAS Companion Inhibitors for use in combination treatment strategies. As a new member of the Revolution Medicines team, you will join other outstanding Revolutionaries in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.

We are pioneering a data-driven discovery and development ecosystem that integrates chemistry, biology, and digital innovation to accelerate insight generation across the R&D continuum — from discovery to clinical development and commercialization.

As the founding Vice President, Head of Data Product Management, this role represents a unique opportunity to define and scale a global data product ecosystem that powers the company’s scientific, clinical, commercial, medical affairs, HEOR/RWE, and patient services excellence. This role sits at the intersection of science, technology, and business, enabling data-driven decision making from early research through clinical development and full commercialization.

Reporting to the Chief Digital Officer, you will operate within a hub-and-spoke model, where the central hub drives enterprise-level data product strategy, standards, and architecture, and the spokes consist of Data Product Managers embedded within key functions (Research, Clinical Development, PDM, Commercial, and G&A) who bring deep domain expertise.

You are both visionary and hands‑on, capable of designing enterprise‑ready data products that power discovery, operational execution, and commercial impact.

• Define and Execute the Global Data Product Vision

  • Develop a unified, enterprise-wide data product strategy spanning discovery, translational science, clinical development, commercialization, medical affairs, HEOR, market access, marketing analytics, and patient services.

  • Define and maintain data product lifecycle frameworks, including schema evolution, version control, metadata standards, and data governance.

  • Build, mentor, develop, recruit, and retain talent in your teams; provide leadership to direct reports and non‑direct report team members. Ensure training, career development, and performance management.

  • Establish and maintain an Enterprise Data Product Catalog and MDM solution covering chemical entities, assay and assay data, biology samples, in vitro and in vivo studies, patient data, real‑world datasets, customer and HCP data, market access and payer data, medical insights, and patient service interactions.

  • Drive adoption of API‑first data contracts to ensure interoperability, reproducibility, and automation across scientific, commercial, and medical systems.

• Develop and govern agent‑ready interfaces (e.g., MCP‑based tools) that expose data products to AI assistants and automation workflows in a secure, auditable manner.
• Lead the Hub‑and‑Spoke Operating Model

  • Build and manage the central Data Product Management function responsible for architecture, design patterns, product governance, and enterprise alignment.

  • Partner with embedded Data Product Managers across Research, Clinical, Commercial, Medical Affairs, HEOR/RWE, Market Access, and Patient Services to ensure each domain’s needs are served while meeting global standards.

  • Align cross‑functional stakeholders across Research, Data Science, IT, Clinical, Commercial, Medical Affairs and G&A to ensure consistent data strategies and product usage.

• Deliver AI‑and‑ML‑Optimized Data Products

  • Design and oversee modular, scalable data products that serve multiple use cases: analytics, AI model training, GenAI fine‑tuning, and operational decision support.

  • Collaborate with Data Engineering, Cloud Ops, MLOps, and Architecture teams to ensure that data products are optimized for high performance, security, and scalability.

  • Ensure all data products are compatible with modern AI‑driven applications and can fuel predictive modeling and large language model (LLM) training.

• Integrat…