External Research Data Governance Lead

Role Summary

External Research Data Governance Lead role within the Data, Automation and Predictive Sciences (DAPS) function to advance data-as-an-asset strategies, drive research productivity and innovation for patients. Responsible for ensuring external data engagements align with scientific objectives, ethical standards, and regulatory expectations, evolving with GSK’s external data ecosystem. Collaborates across multiple teams to integrate governance into research workflows and contractual frameworks, ensuring data utility, integrity, compliance, and participant privacy.

Responsibilities

• External Data Engagement Support: Partner with research teams to evaluate and advise on the type, structure, format, and utility of external data assets; provide guidance on data acquisition, licensing, and access models; embed governance-by-design controls across data ingestion, integration, and reuse; ensure data used in AI model training and computational workflows meets standards for traceability, rights management, and reproducibility.
• Contractual and Compliance Governance: Collaborate with Legal, Procurement, and Business Data Owners to review data schedules; document data use rights, reuse/sharing restrictions, retention, and ownership terms; identify data/IP risks and provide governance recommendations to mitigate misuse, leakage, or compliance exposure; align with global privacy regulations and internal ethical standards.
• External Data Governance Frameworks: Drive consistent governance practices for data from academic collaborations, partnerships, public repositories, or third-party vendors; develop and maintain policies, standards, and playbooks for external data assessment, onboarding, and lifecycle management; champion FAIR and ALCOA+ principles for externally sourced datasets.
• Governance, Ethics, and Privacy Leadership: Support the Head, Research Data Office in shaping positions on data ethics, AI governance, and open data policies; drive compliance with data protection regulations, and align research governance with privacy and AI ethics frameworks; promote an ethical data culture with responsible reuse and transparency.
• Cross-Functional Collaboration and Thought Leadership: Bridge scientific/digital domains with Procurement and Legal, translate governance into actionable data governance expectations; partner with data owners, computational scientists, and platform teams to streamline governance processes; represent Research in enterprise data governance engagements and stay abreast of external standards and policy changes; lead training on data rights, responsible reuse, and computational governance.

Qualifications

• Required: Advanced degree (MSc, PhD, JD, or equivalent) in Life Sciences, Data Science, Bioinformatics, Computer Science, Biological/Chemical Engineering, or related field; minimum 5 years’ relevant experience in research data governance, digital R&D, or computational research operations in pharmaceutical/biotech settings. 8+ years or PhD with 5+ years of applied experience preferred for senior roles.
• Knowledge of research data modalities (omics, imaging, clinical, real-world, structural biology, AI training datasets, public data repositories) and related metadata standards.
• Experience navigating data licensing, contractual data rights, and ethical data use frameworks; familiarity with data privacy laws (GDPR, HIPAA, EHDS, and country-specific legislation) and IP protection principles in research collaborations.

Preferred Qualifications

• Excellent stakeholder management across scientific, legal, procurement, compliance, and digital domains.
• Strong understanding of research data modalities and metadata standards; pragmatic, solution-oriented with ability to balance scientific agility and compliance; experience embedding FAIR, ALCOA+ and Governance-by-design into data management and computational workflows.
• Strategic thinker able to translate governance principles into practical implementation guidance for research environments.