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 companys 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 Revolutionariesin 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 companys 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 domains 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 integrity and compliance.

  • Define and enforce metadata lineage and security requirements for all external data to ensure harmonization with RevMeds internal data ecosystem.

  • Partner with Legal Compliance and Procurement to align external data sharing and usage agreements with enterprise data governance policies.

• Govern Measure and Evangelize Data Products

  • Lead the Data Product Council to set priorities establish governance and drive organizational alignment.

  • Define and monitor leading and lagging indicators to track adoption quality and business impact of data products.

  • Champion data culture and literacy across the organization ensuring scientific and analytical workflows are data product driven.

• Partner Across RevMed and the Broader Ecosystem

  • Collaborate with internal technology research data scientists analysts and business partners to ensure enterprise data strategy alignment.

  • Engage with external SaaS and data partners (e.g. Benchling Genedata Mosaic IQVIA Komodo Health SteepRock CDD Vault Knime Veeva Posit Databricks etc.) to expand data and application capabilities.

  • Continuously evaluate and integrate new technologies that enhance the data product portfolio including GenAI tools data mesh architectures and modern cataloging systems.

• Identify evaluate and integrate high-value public and open-source biomedical data assets (e.g. protein structure and language model resources large-scale omics and drug-response datasets) to augment RevMeds internal data ecosystem while ensuring proper licensing provenance and scientific alignment.

Example Data Products in Context

• Design-Make-Test-Analyze (DMTA) Data Product: Harmonized chemical and biological entity data structure-activity relationships and linked assay metadata.

• Assay Data Product: Standardized experimental output data enabling cross-assay comparisons and downstream analytics.

• Sample & Chain-of-Custody Product: Tracks biological materials and derived datasets from lab bench to clinical study with full traceability.

• Translational Data Product: Unified view of preclinical and clinical data including design endpoints and outcomes.

• CRO Data Exchange Product: Standardized interfaces for ingestion validation and integration of CRO-generated data.

• Omics Integration & Analysis Data Product:Curated and harmonized genomics transcriptomics proteomics and metabolomics datasets designed to support exploratory analyses biomarker discovery and AI-driven feature extraction.

• Insights & ML-Ready Product: Optimized datasets for analytics predictive modeling and AI/GenAI applications.

• Protein Folding AI Model Data Product: Datasets and pipelines supporting the training and validation of next-generation protein structure prediction models

• Customer & HCP 360 Data Product: Integrated prescribing engagement segmentation and behavioral datasets.

• Market Access & Payer Analytics Product: Coverage reimbursement claims and payer dynamics data.

• Patient Journey & Support Services Product: Time-to-therapy adherence discontinuation drivers and support-program impact.

• HEOR/RWE Outcomes Product: Claims EMR registry genomic and survival datasets for real-world value demonstration.

• Medical Affairs Evidence & Insights Product: Engagement insights scientific influence mapping congress intelligence.

Required Skills Experience and Education:

• 15 years of experience in data product management or data platform leadership in life sciences diagnostics or biopharma.

• Proven record of designing and managing enterprise-grade data products supporting research development and commercialization.

• Deep understanding of the scientific data lifecycle and systems used across discovery translational and clinical domains.

• Demonstrated experience in defining API contracts managing evolving schemas and overseeing metadata frameworks.

• Expertise in cloud-native data platforms (AWS Azure Snowflake Databricks) and modern data governance practices.

• Experience integrating data from CROs CDMOs and research partners with rigorous quality and compliance controls.

• Strong technical fluency and ability to communicate across both scientific and engineering stakeholders.

• Skilled in data cataloging metadata management data lineage privacy and security.

• Excellent leadership and change management skills with a history of influencing across functions.

Preferred Skills:

• Advanced degree in Computer Science Bioinformatics or related technical discipline; graduate training in Life Sciences a plus.

• Direct experience in oncology drug discovery or RAS pathway research.

• Familiarity with GenAI and LLM applications in scientific and business domains.

• Experience leading digital transformation or data mesh initiatives in complex organizations

• Experience with GxP requirements and Computer System Validation. #LI-Hybrid #LI-GL1