The External Research Data Governance Lead plays a pivotal role within Data, Automation and Predictive Sciences (DAPS) function, advancing GSK’s data-as-an-asset ambition to drive research productivity and innovation for patients.

As part of the Research Data Office (RDO) team, the role is accountable for ensuring that external data engagements, across partnerships, collaborations, public repositories, and third-party vendors, are aligned with scientific objectives, ethical standards, and regulatory expectations. This responsibility is ongoing, evolving alongside GSK’s growing external data ecosystem and scientific needs.

Positioned within a centralized RDO team, the role requires close collaboration across Research Tech, Therapeutic Areas, AI/ML, R&D Tech (Onyx and Development Data Fabric), Legal, Risk & Compliance, and other business partners. The Associate Director serves as a trusted advisor to these partners, integrating governance into research workflows and contractual frameworks to maximize data utility, integrity, and compliance while safeguarding participant privacy and intellectual property.

As research becomes increasingly computational and data-driven, this role ensures that all external data entering GSK’s research environment is FAIR, interoperable, rights-managed, and IP-protected by design. Through a balance of scientific enablement and governance rigor, the Associate Director helps establish a trusted, compliant, and scalable data ecosystem that underpins responsible in-silico discovery and accelerates medicines and vaccines innovation.

This is an individual contributor role operating within a high-performing, collaborative, and continuously improving team culture, where curiosity, consistency, agility, and quality drive measurable impact and end-user value realization.

Key responsibilities include:

 1. External Data Engagement Support

• Partner with research teams to evaluate and advise on the type, structure, format, and utility of external data assets, ensuring alignment with internal scientific and data management needs.

• Provide guidance on data acquisition, licensing, and access models, including open data, licensed data, collaborations, and consortia.

• Partner with research and computational teams to embed governance-by-design controls across data ingestion, integration, and reuse within digital and in-silico environments.

• Ensure data used in AI model training and computational workflows meets standards for traceability, rights management, and reproducibility.

2.  Contractual and Compliance Governance

• Collaborate with Legal, Procurement, and Business Data Owners to review and clarify data schedules in contractual agreements.

• Ensure accurate documentation of data use rights, reuse and sharing restrictions, retention, and ownership terms.

• Identify potential data and IP risks in external engagements and provide governance recommendations to mitigate misuse, leakage, or compliance exposure.

• Align data acquisition and use practices with global privacy regulations (GDPR, HIPAA, EHDS, and regional equivalents) and internal ethical standards.

3. External Data Governance Frameworks

• Drive consistent governance practices for data obtained through academic collaborations, partnerships, public repositories, or third-party vendors.

• Develop and maintain policies, standards, and playbooks guiding external data assessment, onboarding, and lifecycle management.

• Champion the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) and ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, and Available) principles for externally sourced datasets.

4. Governance, Ethics, and Privacy Leadership

• Support the Head, Research Data office in shaping company positions and responses to emerging data ethics, AI governance, and open data policies.

• Drive compliance with global data protection regulations (GDPR, HIPAA, EHDS, U.S. Data Protection final rule, and country-specific legislation), particularly for human and sensitive data.

• Build alignment between research governance, enterprise data privacy, and AI ethics frameworks to ensure responsible data and model stewardship.

• Promote an ethical data culture—ensuring data reuse, AI model training, and external data collaborations uphold the highest standards of transparency, consent, and trust.

5. Cross-Functional Collaboratio