Director-level individual contributor in Discovery Data Sciences

At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade.

The Data, Automation, and Predictive Sciences (DAPS) function of GSK Research Technologies focuses on large-scale data generation, curation, analysis, and prediction to increase the Probability of Technical and Regulatory Success (PTRS) of assets and unlock upper-quartile ambitions.

As a Director-level individual contributor in Discovery Data Sciences (DDS), you will be a predictive engine for target discovery, bringing cutting-edge machine learning and agentic AI to the breadth of discovery data so that DDS can move beyond analysis toward prediction.

Responsibilities:

• Take pre-trained biological foundation models and adapt them to target biology questions with scientific rigour, fine-tuning, probing, and applying models to questions such as target-disease mechanism.
• Apply and evaluate virtual-cell and in-silico perturbation approaches to predict cellular response and support mechanistic, in-silico target discovery.
• Integrate heterogeneous target evidence into structured, traceable representations, applying knowledge-graph and graph-learning methods.
• Design and build agentic scientific workflows that plan, retrieve, analyse, and synthesise across the target discovery process.
• Evaluate emerging AI/ML approaches for discovery data science, benchmark their performance, and implement them into reliable, production use.
• Apply biological and chemical domain knowledge to ensure models are scientifically grounded, interpretable, and address meaningful therapeutic questions.
• Partner with scientific domain experts to ensure predictive and agentic methods address the questions that matter most to the portfolio.
• Contribute to the MLOps practices that bring predictive methods into reliable use.

Basic Qualifications:

• Master's degree (or higher) in Computer Science, Computational Biology, Bioinformatics, or a related field.
• 15+ years of software engineering and applied computational science experience in bioinformatics, cheminformatics, or scientific computing.
• Experience in pharmaceutical, biomedical, or life sciences preclinical discovery data.
• Experience applying machine learning, graph-based, or AI agent methods to biological or chemical data.
• Experience programming in Python and/or R, including machine learning libraries.
• Experience with production AI/ML systems and MLOps practices.

Benefits: GSK offers a comprehensive benefits program, which can be found at GSK US Benefits Summary.