Director, Computational Statistics, Human Genetics and Genomics

Role Summary

Director, Computational Statistics, Human Genetics and Genomics leads the application of computational statistics and machine learning to genetics and genomics data to inform GSK's drug discovery and development portfolio. The role sits within the Genomic Technologies team in the Human Genetics and Genomics (HGG) department and emphasizes integrating computational, statistical, and multi-omics approaches to interpret genetics and genomics data. Responsibilities include identifying causal gene-phenotype relationships, causal mechanisms, biomarkers, disease subtypes, and patient subgroups, using biobank data, GWAS/xQTL statistics, and integrated analyses. On-site presence is required at one of GSK's sites in the US (PA or MA), UK (Stevenage), or Germany (Heidelberg).

Responsibilities

• Maintain a clear working understanding of the scientific needs of key partners in applied and translational teams across HGG, in experimental Target Discovery, in GSK disease area Research Units, and in Clinical Development.
• Maintain leading edge understanding of mature and emerging capabilities in computational statistics and in statistical/machine learning, both externally and internally (through collaboration with GSK Biostatistics, Data Automation and Predictive Sciences, and AIML departments).
• Identify scientific questions within the broad area of human genetics and genomics in drug discovery and development, that will impact GSK portfolio and pipeline decision-making, and formulate these as statistical problems.
• Develop suitable computational methods to address these questions, and implement robust software that scales in a cloud compute environment. Make individual scientific and technical contributions, inspire, guide, and develop team members, and plan and assess the (human and computational) resources necessary to achieve this.
• Contribute to a culture of innovation, quality, and continuous learning and improvement within the team.

Qualifications

• Required: Advanced degree (PhD or equivalent) in a relevant scientific discipline.
• Required: Substantial research experience (academic or industry) applying statistical approaches to drug discovery or development using genomic/genetic data; experience evaluating, improving, testing, and/or developing statistical or machine learning methods.
• Required: Strong programming skills in R and/or Python; familiarity with reproducible research, literate programming, FAIR data principles, and agile software development; proficiency in analyzing very large datasets with distributed or cloud computing; familiarity with high-performance libraries and tools for large data.
• Required: Strong theoretical understanding of computational statistics and machine learning, with ability to apply these principles to solve scientific questions that may not map to existing solutions.
• Required: Excellent communication, collaboration, influencing and leadership skills; demonstrated delivery of complex and impactful projects and coordination of multidisciplinary teams.

Skills

• Programming in R and/or Python; experience with reproducible research practices, literate programming, FAIR data principles, and agile software development.
• Proficiency in analysis of very large datasets using distributed or cloud computing technologies; familiarity with high-performance libraries and tools for large data.
• Strong theoretical understanding of computational statistics and statistical and machine learning methods.
• Excellent communication, collaboration, influencing and leadership abilities; track record of delivering complex, high-impact projects and coordinating multidisciplinary teams.

Education

• PhD or equivalent in a relevant scientific discipline.

Additional Requirements

• On-site office presence required (minimum of two days a week) at one of GSK’s US (PA or MA), UK (Stevenage) sites or in Germany (Heidelberg).