Principal Scientist, Genomic Technologies

Role Summary

Principal Scientist, Genomic Technologies in GSK's R&D Translational Sciences. The Genomic Technologies team combines computational and statistical methods to enable analytical insight and interpretation of genetics and genomics data, informing portfolio and pipeline decisions. The role focuses on discovering causal gene-phenotype links, identifying causal mechanisms and patient subgroups, and predictive modelling using genetic and multi-omic data. Location: UK (Stevenage, Cambridge, Heidelberg Office) and USA (Upper Providence, PA) with a hybrid working model.

Responsibilities

• Generate large-scale analysis outputs, and/or implement state-of-the-art tools for dynamically analysing, interpreting, and visualizing genetic and genomic data.
• Evaluate, improve, test, and develop production implementations of best-in-class methods for analysis of genetics and genomics data, in collaboration with scientists in Translational Sciences, Biostatistics, Data Automation and Predictive Sciences, or/and AI/ML teams.
• Collaborate with partners across TSci, in experimental Target Discovery, and in GSK disease area Research Units, to influence portfolio and pipeline decisions.
• Stay informed about recent research in the field and consider its potential for application within GSK.
• Contribute to a culture of innovation, quality, and willingness to learn and improve.

Qualifications

• Required: Advanced degree (PhD or equivalent) in a relevant scientific discipline.
• Required: Experience with genetic, genomic, epigenomic, or experimental/functional genomic data, and with state-of-the-art methods for data analysis and interpretation.
• Required: Excellent programming skills in R and/or Python, and application of techniques in reproducible research, literate programming, FAIR data principles, or software development.
• Required: Experience of evaluating, improving, testing, and/or developing methods for analysis and interpretation of large-scale genetics or genomics data.
• Required: Strong communication and team-working skills.
• Preferred: Experience with machine learning and/or advanced statistical methods.
• Preferred: Experience working in multidisciplinary teams on complex and impactful projects.
• Preferred: Experience with integrating information across multiple genetic, genomic, epigenomic, or experimental/functional genomic data types.
• Preferred: Experience with analysis of very large datasets using distributed or cloud computing technologies (e.g. SQL, PySpark, BigQuery, Docker, Nextflow).