At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people over the next 10 years. R&D is committed to discovering and delivering transformational vaccines and medicines to prevent and change the course of disease. Science and technology are coming together in a way they never have before, and we have strong tech-enabled capabilities that allow us to build a deeper understanding of the patient, human biology, and disease mechanisms, and transform medical discovery. We are revolutionising the way we do R&D. We’re uniting science, technology, and talent to get ahead of disease together. Find out more: Our approach to R&D. Principal/Scientific Investigator, Genomic Technologies, Human Genetics and Genomics This position may be based at a GSK R&D site in UK (Stevenage), in USA (Cambridge Tech Square, MA; or Upper Providence, PA), or in Germany (Heidelberg). We are seeking innovative scientists to join our team as Scientific / Principal Investigators in Genomic Technologies. Within the Human Genetics and Genomics (HGG) department at GSK, the Genomic Technologies group provides deep and broad expertise in computational and statistical methods and techniques, to enable best-in-class analytical insight and interpretation of genetics and genomics data, impacting GSK drug discovery and development portfolio and pipeline decisions. At GSK, we value the strength diversity brings to our team and strive to create an inclusive environment where everyone can thrive. We encourage applications from candidates of all backgrounds, and invite candidates to share how their personal qualifications, skills and experience align with the responsibilities and requirements outlined below. They will join existing teams within Genomic Technologies and will work on projects aligned to current areas of focus, including: Discovery of new causal gene-phenotype links through analysis of large-scale individual level human genetic data and GWAS/xQTL summary statistics, and predictive modelling approaches to integrate genetic and ‘omic data. Inference of causal networks and disease-relevant mechanisms through analysis and integration of cellular, transcriptomic, and multi-omic data from genome-scale gene editing perturbation experiments. Identification of causal genes, disease-relevant mechanisms and cell types, potential drug targets, candidate biomarkers, patient subgroups and disease subtypes, through integration of genetic association data across multiple ‘omic and phenotypic layers. In this role you will be… Generating large-scale analysis outputs, and/or implementing cutting-edge or innovative methods for dynamically analysing, interpreting, and visualizing genetic and single cell genomic data. Evaluating, improving, testing, and developing production implementations of best-in-class methods for analysis of genetics and genomics data, in collaboration with scientists in Data and Predictive Sciences, Biostatistics, or/and AIML teams. Collaborating with partners in applied and translational teams across HGG, in experimental Target Discovery, and in GSK disease area Research Units, to influence portfolio and pipeline decisions. Staying informed about recent research in the field and considering its potential for application within GSK. Contributing to an inclusive culture of innovation, quality, and continuous learning. Why you? Required Qualifications & Skills: Advanced degree (PhD or equivalent) in a relevant scientific discipline. Leading and proven expertise in evaluating, improving, testing, and developing methods for analysis and interpretation of large-scale genetics and single cell genomics data. Excellent programming skills in R or python, and knowledge of techniques in reproducible research, literate programming, FAIR data principles, or software development. Several years of experience with genetic, genomic, epigenomic, or experimental / functional genomic data, and with state-of-the-art methods for data analysis and interpretation. Expertise with integrating information across multiple genetic, genomic, epigenomic, or experimental / functional genomic data types. Multiple first-author publications in leading journals. Preferred Skills & Experience: Experience with machine learning and/or advanced statistical methods. Experience with analysis of very large datasets using distributed or cloud computing technologies (e.g., SQL, PySpark, BigQuery, Docker, Nextflow). Experience contributing to multidisciplinary teams on complex and impactful projects. Closing date: 26th February 2025 Please take a copy of the Job Description, as this will not be available post closure of the advert. Successful candidates will detail in their applications how their personal qualifications, skills and experience make them ideally suited to the specific responsibilities and requirements of the role. Why GSK? Uniting science, technology and talent to get ahead of disease together. GSK is a global biopharma company with a special purpose – to unite science, technology and talent to get ahead of disease together – so we can positively impact the health of billions of people and deliver stronger, more sustainable shareholder returns – as an organisation where people can thrive. We prevent and treat disease with vaccines, specialty and general medicines. We focus on the science of the immune system and the use of new platform and data technologies, investing in four core therapeutic areas (infectious diseases, HIV, respiratory/ immunology and oncology). Our success absolutely depends on our people. While getting ahead of disease together is about our ambition for patients and shareholders, it’s also about making GSK a place where people can thrive. We want GSK to be a place where people feel inspired, encouraged and challenged to be the best they can be. A place where they can be themselves – feeling welcome, valued, and included. Where they can keep growing and look after their wellbeing. So, if you share our ambition, join us at this exciting moment in our journey to get Ahead Together. As an Equal Opportunity Employer, we are open to all talent. In the US, we also adhere to Affirmative Action principles. 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