Principal Scientist, Genomic Technologies, Human Genetics and Genomics

Role Summary

Principal Scientist, Genomic Technologies, Human Genetics and Genomics. Join the Genomic Technologies team within the HGG department to integrate computational and statistical methods for analytical insight and interpretation of genetics and genomics data to guide drug discovery and development. Work within an existing Statistical Genetics team on projects focusing on causal gene-phenotype links, integration across multi-omic layers, and predictive modelling of gene-phenotype relationships.

Responsibilities

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

Qualifications

• Advanced degree (PhD or equivalent) in a relevant scientific discipline.
• Experience with large-scale biobank data, such as UK Biobank and All of Us.
• Excellent programming skills in R or Python, with application of reproducible research, literate programming, FAIR data principles, or software development.
• Experience evaluating, improving, testing, and/or developing methods for analysis and interpretation of large-scale genetics data.
• Strong communication and team-working skills.

Preferred Qualifications

• Deep understanding of statistical genetics methodology.
• Experience in cloud-based Trusted Research Environments.
• Experience with machine learning and/or advanced statistical methods.
• Experience working in multidisciplinary teams on complex projects.
• Experience analyzing very large datasets using distributed or cloud computing technologies (SQL, PySpark, BigQuery, Docker, Nextflow).

Education

• PhD or equivalent in a relevant scientific discipline.