Company Overview

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 medicines and vaccines, 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. GSK has kicked off 2025 with impressive growth in sales, profits, and earnings. We anticipate five major new FDA product approvals and 15 phase III/pivotal study readouts over the next two years. So far, we have secured two FDA approvals, with three more expected later this year. By uniting science, technology, and talent, we are committed to getting ahead of disease together. 

Job Purpose

We are seeking an innovative and experienced scientist to join our team.  The successful candidate will join the Genomic Technologies team within the Human Genetics and Genomics (HGG) department at GSK, which integrates deep and broad expertise in computational and statistical methods and techniques, to enable best-in-class insight and interpretation of genetics and genomics data, to guide GSK’s drug discovery and development portfolio and pipeline decisions across therapeutic areas.

We value collaborative working, and scientific evaluation of different approaches to improving our capabilities in areas of focus for Genomic Technologies, including:

• Discovery of new causal gene-phenotype links, by advancing methodology for analysis of large-scale biobank individual human genetic data, and of GWAS/xQTL summary statistics.

• Identification of causal mechanisms, candidate biomarkers, disease subtypes and patient subgroups, by analysing and integrating genetic, multi-‘omic, and phenotypic data from human populations and from genome-scale gene editing perturbation experiments.

• Predictive modelling of presence, direction and magnitude of causal gene-phenotype links, using genetic and ‘omic data, mechanistic annotation, inferred causal networks, and genome-scale perturbation data.

We encourage applicants to describe in their cover letters how their particular qualifications, skills and experience would make them outstanding contributors to the work of Genomic Technologies and of HGG.

PLEASE NOTE: This career opportunity requires an on-site office presence (minimum of two days a week) in one of GSK’s US (PA or MA), UK (Stevenage) sites or in Germany (Heidelberg).

Key responsibilities:

This role will provide YOU the opportunity to lead key activities to progress YOUR career. Responsibilities include:

• Maintain a clear working understanding of the scientific needs of key partners in applied and translational teams across HGG, in experimental Target Discovery, in GSK disease area Research Units, and in Clinical Development.

• Maintain leading edge understanding of mature and emerging capabilities in computational statistics and in statistical/machine learning, both externally and internally (through collaboration with GSK Biostatistics, Data Automation and Predictive Sciences, and AIML departments).

• Identify scientific questions within the broad area of human genetics and genomics in drug discovery and development, that will impact GSK portfolio and pipeline decision-making, and formulate these as statistical problems.

• Develop suitable computational methods to address these questions, and implement robust software that scales in a cloud compute environment.  Make individual scientific and technical contributions, inspire, guide, and develop team members, and plan and assess the (human and computational) resources necessary to achieve this.

• Contribute to a culture of innovation, quality, and continuous learning and improvement within the team.

Basic Qualifications:

• Advanced degree (PhD or equivalent) in a relevant scientific discipline.

• Substantial research experience (either academic or industry) demonstrating innovative application of statistical approaches to answer scientific questions relevant to drug discovery or development using genomic/genetic data.  Research experience critically evaluating, improving and testing, and/or developing statistical/machine learning methodology.

• Strong programming skills in R and/or Python. Familiarity with techniques in reproducible research, literate programming, FAIR data principles, and agile software development. Proficiency in analysis of very large datasets using distributed or cloud computing technologies.  Familiarity with advantages and limitations of high-performance libraries and tools for large data.

• Strong theoretical understanding of computational statistics, and of statistical and machine learning, with the ability to apply these principles to solve scientific questions that do not easily map to existing solutions.

• Excellent communication, collaboration, influencing and leadership skills. Demonstrated delivery of complex and impactful projects, and coordination of multidisciplinary te

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