Principal Scientist, Applied and Translational Omics

Overview

Site Name: USA - Pennsylvania - Upper Providence, UK - Hertfordshire - Stevenage

Posted Date: Feb 3 2026

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.

Find out more:

Our approach to R&D

Position Summary

You will lead applied and translational omics analyses that turn complex biological data into clear decisions. You will work with experimental scientists, data engineers, and clinical teams to design analyses and integrate multi-omic and clinical data. We value clear communication, practical problem solving, curiosity, and collaboration. This role offers visible impact, career growth, and aligns with our mission of uniting science, technology and talent to get ahead of disease together.

This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:

• Design and run reproducible analyses of genomic, transcriptomic, proteomic and other omics datasets.
• Integrate multi-omic and clinical data to prioritize targets, nominate biomarkers, and define patient subgroups.
• Build, validate and product ionize analysis pipelines and tools that support cross-functional teams.
• Communicate results clearly and make practical recommendations to project teams and decision-makers.
• Collaborate with laboratory and clinical colleagues to design analyses that guide experiments and studies.
• Ensure data quality, metadata standards, documentation and reproducible research practices.

Key Responsibilities:

• Lead hands-on analysis of large, diverse omics datasets, including bulk and single-cell data.
• Apply statistical methods, machine learning, and causal inference to answer translational questions.
• Develop scripts, workflows, and software artifacts that follow reproducible research and FAIR principles.
• Troubleshoot data issues, perform rigorous quality control and document analysis steps.
• Mentor junior scientists and contribute to a collaborative and respectful team culture.
• Monitor new methods and tools and evaluate them for adoption in projects.
• 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.
• Help drive the evaluation and development, and lead the application, of analytics methods for spatial and single cell omics in the context of clinical studies.
• Work within or lead cross-functional project teams with GSK scientists and external collaborators, with a focus on impacting clinical study and biomarker decisions across respiratory, immunology, infectious disease, neurodegeneration or metabolic 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.

• Advanced degree (Master’s with relevant experience or PhD preferred) in computational biology, bioinformatics, genetics, biostatistics, or related field.
• Programming experience in R and/or Python for data analysis and scripting.
• Experience processing and analyzing large omics datasets, including quality control and normalization.
• Experience with common genomics file formats and tools (for example VCF, BAM/FASTQ, single-cell data structures).
• Experience reproducible analyses and use of version control (for example Git).
• 1+ years of experience with single-cell and spatial omics analysis workflows