The posting
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. Our R&D focuses on discovering and delivering vaccines and medicines, combining our understanding of the immune system with cutting-edge technology to transform people’s lives. GSK fosters a culture ambitious for patients, accountable for impact, and committed to doing the right thing, making sure that we focus our efforts on accelerating significant assets that meet patients’ needs and have the highest probability of success. We’re uniting science, technology, and talent to get ahead of disease together.
For candidates seeking to be located at our Stevenage site, this role will temporarily be based at Stevenage. However, 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.
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. This is a fully hands-on technical role: you will personally design, build and ship the agentic workflows. You will help build the scientific intelligence that surfaces, evaluates, and prioritises drug targets with confidence, anchoring the team’s long-term Target Discovery Engine ambition.
You will work closely with domain specialists and engineering teams to research, embed, and operationalise predictive and agentic methods across the breadth of discovery data, from omics and functional genomics to chemogenomic and quantitative pharmaceutical-science data.
This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:
- Take pre-trained biological foundation models and adapt them to target biology questions with scientific rigour, fine-tuning, probing, and applying models (e.g. transcriptomic, high-content imaging) 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 (omics, functional genomics, causal biology, chemogenomics, literature, clinical) into structured, traceable representations, applying knowledge-graph and graph-learning methods that let both scientists and agents reason over the evidence.
- Design and build agentic scientific workflows that plan, retrieve, analyse, and synthesise across the target discovery process, using current orchestration patterns (multi-agent systems, RAG, MCP-based tool access) with grounding, provenance, and rigorous evaluation of agent reliability.
- Evaluate emerging AI/ML approaches for discovery data science, benchmark their performance with appropriate validation strategies, 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, communicating evaluation and validation strategy along with its uncertainty.
- Partner with scientific domain experts to ensure predictive and agentic methods address the questions that matter most to the portfolio, and advocate for the opportunities afforded by these technologies.
- Contribute to the MLOps practices that bring predictive methods into reliable use, in collaboration with engineering teams, including model deployment, monitoring, and drift detection for robust model serving.
Why You?
Basic Qualifications We are seeking professionals with the following required skills and qualifications to help us achieve our goals
- 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 such as scikit-learn, PyTorch, or scverse.
- Experience with production AI/ML systems and MLOps practices (CI/CD, containerisation, cloud deployment).
Preferred Qualifications If you have the following characteristics, it would be a plus
- Fine-tuning or applying biological foundation models to target biology questions.
- Virtual-cell, in-silico perturbation, or perturbation-prediction methods, with an understanding of how to validate their reliability.
- Building or deploying agentic workflows (multi-agent orchestration, RAG, MCP-based tool access, foundation-model integration).
- An instinct for matching the right method to the scientific question, and for what makes a prediction credible enough to act on.
- Knowledge-graph or graph-learning methods for integrating heterogeneous evidence.
- Productionising ML models, model monitoring, and automated retraining; hands-on MLOps (versioning, experiment tracking, containerisation).
- Deep-learning architectures for biological data (transformers, diffusion models, GNNs), and data engineering concepts (ETL, databases, data lakes).
- Strong communication skills, with the ability to translate between biological and technical audiences.
Skills
Artificial Intelligence (AI), Computational Sciences, Computer Programming, Data Analysis, Data Management, Data Modeling, Data Science, Predictive Modeling, Statistical Models
Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.
Why GSK?
Uniting science, technology and talent to get ahead of disease together.
GSK is a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together. We aim to positively impact the health of 2.5 billion people by the end of the decade, as a successful, growing company where people can thrive. We get ahead of disease by preventing and treating it with innovation in specialty medicines and vaccines. We focus on four therapeutic areas: respiratory, immunology and inflammation; oncology; HIV; and infectious diseases – to impact health at scale.
People and patients around the world count on the medicines and vaccines we make, so we’re committed to creating an environment where our people can thrive and focus on what matters most. Our culture of being ambitious for patients, accountable for impact and doing the right thing is the foundation for how, together, we deliver for patients, shareholders and our people.
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GSK is an Equal Opportunity Employer. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), parental status, national origin, age, disability, genetic information (including family medical history), military service or any basis prohibited under federal, state or local law.
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