Director, R&D Data Governance

Position Summary

This role is the first R&D-focused data governance position within Alkermes’ new Enterprise Data Management function and will play a foundational role in shaping how R&D data is governed, enabled, and scaled across the organization. Reporting to the Executive Director, Enterprise Data Management, you will support the establishment of the new function as well as help to define and operationalize the strategy and operating model that ensure R&D data is trusted, discoverable, secure, and usable across discovery, preclinical, and clinical domains. You will co-develop and operationalize the R&D data vision, multi-year roadmap, and governance framework—including data acquisition, curation, quality, metadata, and access controls—so scientists and analysts can reliably self-serve data while maintaining compliance and integrity. You will partner closely with R&D, IT, and enterprise Data & AI teams to make recommendations, align with functional leaders, and implement scalable data management capabilities that embed governance into day-to-day workflows. In this role, you will serve as a key connector across functions, translating scientific and business needs into recommendations around durable data products, policies, and processes that accelerate decision-making and innovation.

Essential Areas of Responsibility

R&D Data Strategy: 

• Guide the process to define and communicate the R&D data vision and maintain a multi-year roadmap spanning governance, architecture alignment, acquisition, curation, and data utilization across critical R&D domains (e.g., discovery, translational research, biomarkers).
• Collaborate with the Director, Enterprise Data Governance and R&D leaders to co-develop, recommend, and operationalize R&D data governance frameworks, operating models, and processes, including forums, decision rights, escalation paths, and success metrics.
• Work with R&D, IT, and Data & AI leaders to align priorities, sequence investments, and ensure governance enables scientific outcomes.
• Drive adoption of FAIR data principles through pragmatic standards, embedded workflows, and data literacy programs.
• Partner with R&D leaders to identify, procure, and document R&D data assets and capabilities needed to support priority scientific and business use cases, making recommendations and gaining alignment on scope, sequencing, and implementation approaches

Data Standards, Stewardship, and Quality:   

• Establish and lead R&D data governance operating model and processes including implementing metrics to monitor governance effectiveness and data maturity; Partner to implement R&D data stewardship policies, roles, and responsibilities
• Define and establish policies to enable R&D data quality standards (e.g., metadata, ontologies, controlled vocabularies, etc.)  with the Data Governance Committee
• Collaborate on evaluation, selection, and implementation of data management platforms and tools (e.g., data catalogs, access control systems, data lakes), and identify places where R&D can develop fit-for-purpose platforms using existing enterprise tools
• Define procedures for data validation, verification, and monitoring to ensure accuracy, completeness, and traceability; Implement metadata management, data lineage, and data quality practices
• Work closely with the INDIGO program and IT to recommend and help implement data standards, metadata, quality practices, and access patterns that ensure R&D data assets are AI‑ready and suitable for intended analytical and AI/ML use cases

R&D Business Enablement:

• Provide guidance and training to R&D teams on data governance best practices, and act as the primary liaison between R&D, IT, and enterprise data management functions  
• Partner with data management and engineering teams to ensure effective ingestion, curation, and integration of R&D datasets (e.g., omics, imaging, clinical, etc.)
• Partner with IT and compliance teams to recommend and implement access controls that ensure appropriate data use and compliance with internal policies and external regulations with the goal of enabling self-service data access for scientists and analysts while maintaining data integrity and security
• Support and c