Director, R&D Data Governance

Alkermes

About The Position

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.

Requirements

Desire to shape the future of data-driven R&D innovation and collaborate with leading scientists, clinicals, and digital experts.
Thrives in a fast-paced impact driven organization where your contributions are visible and meaningful.
Self-starter with the ability to work with minimum supervision.
Ability to multitask – effectively manage simultaneous work requests across departments, IT, and other cross-functional organizations.
Willingness to learn new applications, tools, and approaches as additional needs arise.
Strong work ethic, with a proven track record in successfully achieving goals.
Proactive communication style.
Excellent verbal and written communication skills with the ability to present to and interact with a diverse group of executives, managers, and subject matter experts.

Nice To Haves

Direct experience working with medical claims, EMR, -omics, patent, and other life-science oriented data modalities is a plus.

Responsibilities

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.
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.
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 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.