Director, Translational Data Sciences
GSK
August 11, 2026
Remote friendly (Collegeville, PA)
United States
Clinical Research and Development
Director, Translational Data Sciences
Key Responsibilities
- Develop and execute the Translational Data Sciences strategy, with accountability for portfolio-level outcomes across multiple therapeutic areas (respiratory, renal, hepatology, immune-mediated disease).
- Leverage data sciences for pipeline progress via matrix team leadership and cross-functional collaboration across Translational & Developmental Sciences; apply across therapy areas and broader organizations (including R&D Technologies and AIML).
- Establish and maintain high-value partnerships with academic laboratories, companies, and international consortia; represent the organization at international scientific forums.
- Collaborate with R&D leadership to align computational strategy with clinical development priorities, translating AI-derived insights into actionable hypotheses (target identification/validation, biomarker discovery, patient stratification) to support pipeline delivery and clinical trial success.
- Contribute to external visibility through publications, conference presentations, and academic collaborations.
Basic Qualifications & Skills (required)
- Undergraduate degree in physical sciences or biological sciences, or a medical degree.
- PhD in data science, computer science, computational biology, bioinformatics, or closely related discipline.
- Extensive academic/industrial drug discovery experience; record leading high-impact computational biology programmes from conception through application.
- Prior line or matrix management experience; led interdisciplinary teams across computational, experimental, and translational disciplines.
- Experience structuring/managing strategic partnerships (incl. alliance governance).
- Experience presenting to executive leadership, external partners, and international scientific audiences.
Preferred Qualifications & Skills
- Spatial transcriptomics and single-cell multi-omics expertise; ability to apply and/or develop analytical methods; ideally integrate genomic insights with germline evidence for causal mechanistic insight.
- ML for biology expertise (e.g., GNNs, Bayesian causal inference, topological data analysis, generative models, reinforcement learning).
- Ability to design/deliver agentic AI systems integrating LLM APIs, generative AI frameworks, and automated analysis workflows.
- Translational interface knowledge; adaptive clinical trial design and biomarker-driven patient stratification.
- Experience processing/curating/governing human datasets (data quality, integrity, compliance).
What We Offer
- Competitive senior compensation package; flexible hybrid working; commitment to scientific impact.
Application Instructions
- Closing date: 1 September 2026. Use the cover letter or CV to describe how you meet the competencies outlined above.
Key Responsibilities
- Develop and execute the Translational Data Sciences strategy, with accountability for portfolio-level outcomes across multiple therapeutic areas (respiratory, renal, hepatology, immune-mediated disease).
- Leverage data sciences for pipeline progress via matrix team leadership and cross-functional collaboration across Translational & Developmental Sciences; apply across therapy areas and broader organizations (including R&D Technologies and AIML).
- Establish and maintain high-value partnerships with academic laboratories, companies, and international consortia; represent the organization at international scientific forums.
- Collaborate with R&D leadership to align computational strategy with clinical development priorities, translating AI-derived insights into actionable hypotheses (target identification/validation, biomarker discovery, patient stratification) to support pipeline delivery and clinical trial success.
- Contribute to external visibility through publications, conference presentations, and academic collaborations.
Basic Qualifications & Skills (required)
- Undergraduate degree in physical sciences or biological sciences, or a medical degree.
- PhD in data science, computer science, computational biology, bioinformatics, or closely related discipline.
- Extensive academic/industrial drug discovery experience; record leading high-impact computational biology programmes from conception through application.
- Prior line or matrix management experience; led interdisciplinary teams across computational, experimental, and translational disciplines.
- Experience structuring/managing strategic partnerships (incl. alliance governance).
- Experience presenting to executive leadership, external partners, and international scientific audiences.
Preferred Qualifications & Skills
- Spatial transcriptomics and single-cell multi-omics expertise; ability to apply and/or develop analytical methods; ideally integrate genomic insights with germline evidence for causal mechanistic insight.
- ML for biology expertise (e.g., GNNs, Bayesian causal inference, topological data analysis, generative models, reinforcement learning).
- Ability to design/deliver agentic AI systems integrating LLM APIs, generative AI frameworks, and automated analysis workflows.
- Translational interface knowledge; adaptive clinical trial design and biomarker-driven patient stratification.
- Experience processing/curating/governing human datasets (data quality, integrity, compliance).
What We Offer
- Competitive senior compensation package; flexible hybrid working; commitment to scientific impact.
Application Instructions
- Closing date: 1 September 2026. Use the cover letter or CV to describe how you meet the competencies outlined above.