Computational biologist-Applied and Translational Omics

GSK•Upper Providence Township, PA

About The Position

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 apply computational and analytical skills to translate omics data into insights that drive discovery and development. You will work closely with experimental scientists, data engineers, and clinical teams to design analyses, integrate multi-omic datasets, and support decisions that move targets toward the clinic. We value curiosity, clarity, collaboration, and the ability to communicate complex results in simple, actionable ways. Join us to grow your skills, make a measurable impact, and help unite 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:

Requirements

Advanced degree (PhD) in computational biology, genetics, or biological sciences.
Experience in R and/or Python for data analysis and scripting.
Experience with single-cell, spatial omics, or proteomics analysis workflows.
- Track record of producing reproducible analyses and version-controlled code (e.g., Git).

Nice To Haves

Background in neurodegeneration and knowledge of the neurodegeneration data landscape would be preferred but not strictly required.
Experience integrating omics with clinical or real-world data.
- Familiarity with cloud or distributed compute environments (e.g., BigQuery, Spark, Nextflow, Docker).
Publication record or evidence of influencing research direction through analytical contributions.
Strong communication skills and experience working in multidisciplinary teams

Responsibilities

Design, implement and run reproducible analyses of genetic, transcriptomic, proteomic, and other omics datasets
Integrate multi-omic and clinical data to identify candidate targets, biomarkers, and patient subgroups
Interpret results clearly and present findings to scientists and decision-makers in written and verbal formats.
Collaborate with experimental teams to plan analyses that inform study design and follow-up experiments.
Maintain data quality, metadata standards, and best practices for reproducible research.
Work hands-on with patient derived large-scale and diverse omics datasets, including single-cell and spatial omics
Apply statistical, machine learning, and causal inference methods to answer translational questions.
Develop scripts and workflows following FAIR principles.
- Troubleshoot data issues and ensure rigorous QC and documentation for analyses.
Mentor junior colleagues and contribute to a collaborative, curious, and respectful team culture.
Keep up to date with methods and tools and evaluate new approaches for adoption.