Director, Translational Data Sciences

Director, Translational Data Sciences

We are seeking an exceptional and ambitious scientific leader to serve as Director of Translational Data Sciences. This individual will define and drive our translational data strategy across the continuum from disease biology to clinical readout, integrating multi-omics platforms with AI-driven analytics to bridge the gap between mechanistic discovery and therapeutic decision-making.

The successful candidate will combine deep expertise in spatial, single-cell and/or other multi-omics with a demonstrated record of delivering insights from agentic AI systems. Critically, we are looking for someone who can move between scientific rigour and strategic execution: a leader who communicates directly with R&D and clinical development executives, structures external alliances, and holds themselves and their team accountable to the clinical relevance of every analytical platform they deploy.

This is a rare opportunity for a senior data scientist with clinical and/or basic science training to shape how a leading pharmaceutical organisation connects omics data to patient outcomes, at a moment when the tools to do so are more powerful than ever before.

For candidates seeking to be located at our Stevenage site, this role will temporarily be based ever, the Company plans to relocate its offices to Cambridge, UK. The location of this role will therefore subsequently change to Cambridge, UK in accordance with timelines to be set by the Company. The relocation is currently proposed to take effect by early 2029.

Scientific Strategy.

Develop and execute the Translational Data Sciences strategy, with accountability for portfolio-level outcomes across multiple therapeutic areas including respiratory, renal, hepatology, and immune-mediated disease.

Data Sciences and AI.

Leverage the potential of data sciences for pipeline progress through matrix team leadership, collaboration across Translational & Developmental Sciences and application across therapy areas and the broader organisations across GSK including R&D Technologies and AIML.

Partnership & Alliance Leadership.

Establish and maintain high-value partnerships with leading academic laboratories, companies, and international consortia; represent the organisation at international scientific forums and translate external innovation into internal competitive advantage.

Translational Integration.

Work directly with R&D leadership to align computational strategy with clinical development priorities, ensuring that AI-derived insights are translatable into actionable hypotheses spanning target identification and validation, biomarker discovery, and patient stratification to support pipeline delivery and clinical trial success.

Scientific Visibility.

Contribute to the organisation's external positioning through publications, conference presentations, and academic collaborations.

Basic Qualifications & Skills:

We are looking for professionals with these required skills to achieve our goals:

• Physical sciences (Maths, Computer Science, Physics, Chemistry, Engineering etc) or Biological sciences (Biology, Biochemistry, Bioengineering etc) undergraduate degree or Medical degree
• PhD in data science, computer science, computational biology, bioinformatics, or a closely related discipline;
• Extensive experience spanning academic and/or industrial drug discovery environments, with a demonstrable record of leading high-impact computational biology programmes from conception through to application to drug discovery or development challenges.
• Prior line or matrix management experience and experience leading interdisciplinary teams across computational, experimental, and translational disciplines in both academic and/or industrial settings.
• Demonstrated experience structuring and managing strategic partnerships with academic institutions and companies, including contribution to alliance governance.
• Experience presenting to executive leadership, external partners, and international scientific audiences.

Preferred

Qualifications & Skills:

Please note the following skills are not necessary, just preferred, if you do not have them, please still apply:

• Expert command of spatial transcriptomics platforms and single-cell multi-omics analysis, with fluency in applying established analytical frameworks and the capability to develop novel methods where existing approaches are insufficient. Ideally, also with experience of integration of genomic insights with germline genetic evidence to derive causal mechanistic insight.
• Deep expertise across the full spectrum of modern ML for biology: graph neural networks, Bayesian causal inference, topological data analysis, deep generative models (VAEs, diffusion models, GANs), and reinforcement learning, applied to biological discovery problems.
• Proven ability to design and deliver agentic AI systems integrating large language model APIs, generative AI frameworks, and analysis workflows for automated biological insight…