Principal Scientist, Translational Omics

'For candidates seeking to be located at our Stevenage site, this role will temporarily be based ever, 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

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.

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Our approach to R&D

We are seeking a motivated computational biologist to focus on analysis and integration of diverse and complex multi-omic data to deliver actionable insights that will help bring the right medicines to the right patients. Your work will have application across our pipeline, from multi-omic characterisation of disease endotypes to biomarker discovery and patient stratification, with a focus on hepatology, renal, metabolic and cardiovascular disease.

You will lead and contribute to the analysis and integration of cutting edge high dimensional multi-omic data, including single cell and spatial transcriptomics, proteomics, metabolomics and lipidomics to derive insights that will impact GSK pipeline decisions.

The successful candidate will work in a multidisciplinary, collaborative and scientifically driven environment, interacting with GSK scientists and external collaborators to advance drug discovery and clinical development. Additionally, you will be encouraged and supported to continue to build your scientific profile through presentations, development programmes and peer-reviewed publications.

• Impact the GSK drug development pipeline through development and application of innovative computational and statistical approaches to the analysis, integration and interpretation of spatial, single cell and other cutting edge high dimensional multi-omic data.
• Work within cross-functional project teams with GSK scientists and external collaborators, with a focus on impacting target, biomarker and clinical study decisions across hepatology, renal, cardiovascular disease.
• Effectively communicate analysis findings and recommendations, with expert interpretation, to project teams.
• Work with focus and agility to deliver against objectives, demonstrating strong statistical, analytical and critical thinking skills.

• PhD in Computational Biology, Bioinformatics, Computational Sciences, Machine Learning, or a related field, or a Master's degree with 10+ years of relevant experience in these disciplines.
• Experience analyzing multi-omic data types, including single-cell and spatial omics datasets.
• Experience using tools and frameworks for multi omic data analysis
• Experience programming in R and/or Python.
• Experience applying analytical, statistical, and/or machine learning methods to large-scale biological datasets.

• Ability to derive and apply novel insights.
• Ability to critically evaluate cutting-edge tools and frameworks.
• Track record of writing reproducible and scalable code.
• Strong interpersonal and communication skills.
• Ability to thrive in a matrix environment.
• Demonstrated scientific capability as evidenced by publications, research reports, and external presentations.
• Demonstrated delivery of high quality and state-of-the-art computational biology solutions and analyses in support of drug discovery projects.
• Demonstrated experience in the analysis of multi-omic data linked to clinical phenotypes or measures for disease endotyping, biomarker discovery, mechanism and efficacy analysis and/or patient…