Senior Director Translational Data Sciences

GSK

21h•Hybrid

About The Position

We are a science-led, global pharmaceutical company committed to closing the gap between biological discovery and clinical impact. Our translational mission is to decode the molecular and cellular architecture of human disease, using multi-omics data as the connective tissue between causal mechanistic insight and patient outcome. We are building the analytical infrastructure that turns complex biological signals into actionable clinical hypotheses. We believe that the next generation of translational leadership will be defined by the ability to move fluidly between deep genomic profiling, including at single-cell and spatial resolution, and AI-driven data integration to derive and apply insights to identify and develop transformative medicines for patients. This role has been created for a scientist who can operate at that interface with rigour, creativity, and strategic clarity. Position Overview We are seeking an exceptional and ambitious scientific leader to serve as Senior 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.

Requirements

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 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 management experience and experience building and leading interdisciplinary teams across computational, experimental, and translational disciplines in both academic and industrial settings.
Demonstrated experience structuring and managing strategic partnerships with academic institutions and companies, including contribution to alliance governance.
Familiarity with the translational interface between target biology and clinical development; experience with adaptive clinical trial design and biomarker-driven patient stratification is highly desirable.
Experience presenting to executive leadership, external partners, and international scientific audiences.

Nice To Haves

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 generation.

Responsibilities

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.
Leverage the potential of data sciences for pipeline progress through team leadership, collaboration across Translational & Developmental Sciences and application across therapy areas and the broader organisations across GSK including R&D Technologies and AIML.
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.
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.
Build and mentor an interdisciplinary team of computational biologists, AI/ML engineers, and clinician-scientists and wet-lab scientists; foster a culture of scientific excellence, methodological rigour, and collaborative ambition.
Contribute to the organisation's external positioning through publications, conference presentations, and academic collaborations.