Associate Director, Translational Data Sciences
GSK
August 13, 2026
Remote friendly (Collegeville, PA)
United States
Clinical Research and Development
Position Summary
You will contribute to translational data science work to turn genetics and multi-omics data into actionable insights, partnering with translational scientists, programme teams, AI/ML, and platform colleagues.
Lead key activities including:
- Design, deliver, and interpret translational analyses using genetics/genomics, proteomics, and clinical phenotypes to inform target, biomarker, and patient-selection decisions.
- Own end-to-end analytical workstreams (ingestion, quality control, reproducible pipelines, reporting).
- Translate scientific questions into robust computational approaches; present results via written reports and briefings.
- Partner with AI/ML and platform/bioinformatics teams to operationalise and scale reproducible analysis in secure environments.
- Mentor and coach scientists/analysts; support skill-building and collaborative problem solving.
- Represent the team in cross-functional programme meetings and external collaborations.
Responsibilities
- Deliver reproducible, well-tested pipelines for large-scale genetic and multi-omic datasets.
- Produce high-quality translational interpretations that inform project decisions.
- Ensure analyses meet data governance, privacy, and traceability standards.
- Evaluate and adopt new methods/tools that add translational value.
- Support knowledge sharing (presentations, documentation, peer review).
Qualifications (required)
- Undergraduate degree in physical or biological sciences (or medical degree).
- PhD in data science, computer science, computational biology, bioinformatics, or closely related discipline.
- Hands-on experience with large-scale genetic/multi-omics data integrated with clinical/phenotypic data.
- Strong Python skills; experience delivering reproducible, production-quality analyses (version control, testing, documentation).
- Experience building/owning end-to-end pipelines/workflows.
- Clear communication; able to present to non-specialists.
Preferred
- Translational pharma/biotech/clinical experience influencing project decisions.
- ML/advanced statistics; single-cell/spatial/proteomic/emerging omics.
- Cloud/trusted research environments/scalable compute; peer-reviewed publications.
- Experience leading/mentoring and coordinating cross-functional projects.
How to Apply
Apply and explain how your experience maps to the responsibilities and qualifications.
You will contribute to translational data science work to turn genetics and multi-omics data into actionable insights, partnering with translational scientists, programme teams, AI/ML, and platform colleagues.
Lead key activities including:
- Design, deliver, and interpret translational analyses using genetics/genomics, proteomics, and clinical phenotypes to inform target, biomarker, and patient-selection decisions.
- Own end-to-end analytical workstreams (ingestion, quality control, reproducible pipelines, reporting).
- Translate scientific questions into robust computational approaches; present results via written reports and briefings.
- Partner with AI/ML and platform/bioinformatics teams to operationalise and scale reproducible analysis in secure environments.
- Mentor and coach scientists/analysts; support skill-building and collaborative problem solving.
- Represent the team in cross-functional programme meetings and external collaborations.
Responsibilities
- Deliver reproducible, well-tested pipelines for large-scale genetic and multi-omic datasets.
- Produce high-quality translational interpretations that inform project decisions.
- Ensure analyses meet data governance, privacy, and traceability standards.
- Evaluate and adopt new methods/tools that add translational value.
- Support knowledge sharing (presentations, documentation, peer review).
Qualifications (required)
- Undergraduate degree in physical or biological sciences (or medical degree).
- PhD in data science, computer science, computational biology, bioinformatics, or closely related discipline.
- Hands-on experience with large-scale genetic/multi-omics data integrated with clinical/phenotypic data.
- Strong Python skills; experience delivering reproducible, production-quality analyses (version control, testing, documentation).
- Experience building/owning end-to-end pipelines/workflows.
- Clear communication; able to present to non-specialists.
Preferred
- Translational pharma/biotech/clinical experience influencing project decisions.
- ML/advanced statistics; single-cell/spatial/proteomic/emerging omics.
- Cloud/trusted research environments/scalable compute; peer-reviewed publications.
- Experience leading/mentoring and coordinating cross-functional projects.
How to Apply
Apply and explain how your experience maps to the responsibilities and qualifications.