Principal Scientist, Translational Data Sciences
HybridHeidelberg, Baden-Wurttemberg, Germany or Upper Providence, Pennsylvania, United States
Job Summary
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 prioritize 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. This role involves leading translational data science efforts at GSK to turn human genetics and multi-omic data into clear decisions for drug discovery.
Required Qualifications
- 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
- 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
Desired Qualifications
- 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
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.