Principal Scientist, Translational Omics

About the role

The role will temporarily be based at Stevenage. However, the Company plans to relocate its offices to Cambridge, UK or Ware. The location of this role will subsequently change to Cambridge, UK or Ware in accordance with timelines to be set by the Company.

Responsibilities

• 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.
• Work within cross-functional project teams with GSK scientists and external collaborators, with a focus on impacting target, biomarker and clinical study decisions across hepatology, renal, cardiovascular 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.

Requirements

• PhD or master's degree with 10+ years' experience in computational biology, bioinformatics, computational sciences or machine learning.
• Experience analyzing multi-omic data types, including single-cell and spatial omics datasets.
• Experience using tools and frameworks for multi omic data analysis.
• Experience programming in R and/or Python.
• Experience applying analytical, statistical, and/or machine learning methods to large-scale biological datasets.

Qualifications

• Ability to derive and apply novel insights.
• Ability to critically evaluate cutting-edge tools and frameworks.
• Track record of writing reproducible and scalable code.
• Strong interpersonal and communication skills.
• Ability to thrive in a matrix environment.
• Demonstrated scientific capability as evidenced by publications, research reports, and external presentations.
• Demonstrated delivery of high quality and state-of-the-art computational biology solutions and analyses in support of drug discovery projects.
• Demonstrated experience in the analysis of multi-omic data linked to clinical phenotypes or measures for disease endotyping, biomarker discovery, mechanism and efficacy analysis and/or patient stratification.
• Demonstrated experience in the analysis of multi-omic data from clinical studies and working in a regulatory environment.
• Expertise in statistical approaches to the identification and assessment of predictive and prognostic biomarkers.
• Experience in application of agentic tooling for computational delivery.