Computational Biologist/Principal Scientist, Applied & Translational Omics (m/f/d)

Role Summary

Role: Computational Biologist/Principal Scientist, Applied & Translational Omics (m/f/d). We are seeking a motivated, innovative computational biologist to join our team. The successful candidate will evaluate and apply novel methods for the analysis and integration of spatial and single-cell data to impact the GSK pipeline, with applications across molecular and cellular characterization of disease mechanisms and endophenotypes, biomarker discovery, understanding mechanisms of response and informing precision medicine approaches. The successful candidate will work in a multidisciplinary, collaborative environment with GSK scientists and external collaborators, and will have opportunities to build their scientific profile through presentations, development programs and peer-reviewed publications. Locations include Heidelberg; Stevenage; Upper Providence, USA; Boston, MA (Central, North and South); and other listed sites.

Responsibilities

• Impact the GSK drug development pipeline through development, evaluation and application of innovative computational and statistical approaches to the analysis and interpretation of spatial and single-cell data.
• Contribute to the evaluation and development of best practice methods for spatial and single-cell omics and to the upskilling of others.
• Work within cross-functional project teams with GSK scientists and external collaborators, with an emphasis on respiratory, immunology, neurodegeneration or metabolic disease.
• Effectively communicate analysis findings, with expert interpretation, to project teams.
• Work with focus and agility to deliver against objectives, demonstrating strong statistical, analytic and critical thinking skills.

Qualifications

• Extensive experience in the analysis of single-cell and spatial omics datasets, with proven ability to derive and apply novel insights from these and other emerging genomic technologies.
• Familiarity with, and ability to critically evaluate, the development of cutting-edge tools and frameworks for single-cell and spatial data integration, visualization, and interpretation.
• Strong proficiency in at least one of R and/or Python, with a track record of writing reproducible and scalable code.
• Proven expertise in statistical modelling, hypothesis testing, and data-driven inference.
• Data science skills to collect, integrate, mine, analyse and interpret complex diverse biological data and translate them into testable hypotheses.
• Ability to understand key scientific questions and identify and develop innovative computational solutions to answer them and impact the drug discovery pipeline.
• Demonstrated experience in effectively communicating complex scientific concepts to diverse audiences.
• Demonstrated ability to work effectively in multidisciplinary teams.

Education

• PhD or equivalent experience in a relevant scientific discipline (e.g. computational biology, biomedical/biological sciences, biomedical statistics, computational sciences, bioinformatics or machine learning) with a history of impactful scientific publications and/or presentations.