Principal Scientist, Translational Data Sciences

GSK•Upper Providence, PA

22h•€74,250 - €123,750•Hybrid

About The Position

You will lead translational data science efforts that turn human genetics and multi-omic data into clear decisions for drug discovery. You will work closely with experimental scientists, biostatisticians, data engineers, AI researchers and clinical teams. 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. For candidates seeking to be located at our Stevenage site, this role will temporarily be based at Stevenage. However, 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.

Requirements

Physical sciences (Maths, Computer Science, Physics, Chemistry, Engineering etc) or Biological sciences (Biology, Biochemistry, Bioengineering etc) undergraduate degree or Medical degree.
PhD in data science, computer science, computational biology, bioinformatics, or a closely related discipline.
Strong hands-on experience analysing large-scale genetic and multi-omic datasets.
Proficient programming skills in Python and experience with reproducible workflows and version control.
Experience integrating molecular data with clinical or phenotypic data to answer translational questions.
Proven ability to communicate complex results clearly to scientific and non-technical audiences.
Willingness and ability to work in a hybrid model with regular on-site presence in the United Kingdom as required by the Performance with Choice policy.

Nice To Haves

Track record of peer-reviewed publications or major scientific contributions in genetics or translational genomics.
Experience with cloud or distributed computing platforms and large-scale data processing tools.
Familiarity with causal inference, biomarker discovery, and patient stratification methods.
Experience with single-cell, spatial omics, proteomics or other emerging molecular technologies.
Experience working within multi-disciplinary project teams in industry or academia.
Experience contributing to production-ready pipelines or shared analysis platforms.

Responsibilities

Design and run rigorous, reproducible analyses of large-scale genetic and multi-omic datasets to address translational questions.
Integrate genetics, proteomics, transcriptomics and clinical data to prioritise targets, nominate biomarkers, and define patient subgroups.
Build and maintain analysis workflows and tools following reproducible research and FAIR data principles.
Translate analytic results into clear recommendations for project teams and leaders.
Collaborate across functions and with external partners to shape study design, data generation and interpretation.
Mentor and support junior colleagues to share methods, standards and practical best practice.