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. It will shape how R&D data is governed, enabled, and scaled across the organization, ensuring it is trusted, discoverable, secure, and usable across discovery, preclinical, and clinical domains. The successful candidate will co‑develop the R&D data vision, multi‑year roadmap, and governance framework, and will partner closely with R&D, IT, and enterprise Data & AI teams to embed governance into day‑to‑day workflows.

• Define and communicate the R&D data vision and maintain a multi‑year roadmap covering governance, architecture alignment, acquisition, curation, and data utilisation across critical R&D domains.
• Collaborate with the Director, Enterprise Data Governance and R&D leaders to co‑develop, recommend, and ope rationalise 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 programmes.
• Partner with R&D leaders to identify, procure, and document R&D data assets and capabilities needed for priority scientific and business use cases, recommending and gaining alignment on scope, sequencing, and implementation approaches.
• Establish and lead the R&D data governance operating model and processes, implementing metrics to monitor governance effectiveness and data maturity; partner to implement stewardship policies, roles, and responsibilities.
• Define and establish policies to enable R&D data quality standards (e.g., metadata, ontologies, controlled vocabularies) 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 opportunities to 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.
• Provide guidance and training to R&D teams on data governance best practices, acting 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).
• 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, enabling self‑service data access for scientists and analysts while maintaining data integrity and security.
• Support and contribute to change management efforts to foster a data‑centric culture within R&D, and provide training and support to data users and stakeholders on data management best practices.

• Desire to shape the future of data‑driven R&D innovation and collaborate with leading scientists, clinicians, and digital experts.
• Thrives in a fast‑paced, impact‑driven organization where contributions are visible and meaningful.
• Self‑starter with the ability to work with minimal supervision.
• Ability to multitask and effectively manage simultaneous work requests…