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, integrity, and compliance.

  • Define and enforce metadata, lineage, and security requirements for all external data to ensure harmonization with RevMed’s 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 RevMed’s 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