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.

The Opportunity:

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.

Responsibilities:

• 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, CloudOps, 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.

• Integrate and Harmonize CRO and External Data Sources

  • Develop standardized frameworks and data exchange pipelines with Contract Research Organizations (CROs), academic partners, and external data vendors.

  • Implement automated ingestion and validation workflows to ensure data quality, i