Director, Statistical Genetics

Director, Statistical Genetics within Informatics and Predictive Sciences (IPS) at Bristol Myers Squibb, Cambridge, MA. Hybrid role.

Lead the Target Sciences team by designing, implementing, and overseeing efforts to use innovative approaches to systematically define mechanisms driving disease risk due to genetics. Align with disease area strategy to impact drug discovery and translational efforts using insights from causal human biology, including leading "variant to gene to function" efforts by integrating omics data (scRNAseq, spatial, proteomics) and other functional data with genetic data from large scale cohorts.

Work closely with the broader scientific community through pre-competitive collaborations (e.g. FinnGen, UK Biobank, UK Genes & Health, Alliance for Genomic Discovery), and publish and present industry-leading work.

Responsibilities

• Drive design and implementation of computational strategy to infer causal mechanisms driving disease using human genetics (Mendelian randomization with proteomics, TWAS, colocalization)
• Lead team to perform cross-biobank analyses focused on identifying mechanisms underlying human genetic risk factors (LD score regression, eQTL mapping, scRNAseq)
• Lead team and coordinate with stakeholders across research to facilitate the use of germline genetics for discovery in neuro, immunology, and cardiovascular disease
• Evaluate and prioritize multi-modal, disease-specific datasets for causal human biology of priority disease areas
• Coordinate with stakeholders across research to nominate, evaluate, and advance novel drug targets
• Communicate findings and recommend follow up actions (seminars, project meetings, external publications)

Basic Qualifications

• PhD with 8+ years of academic/industry experience, or Master's with 12+, or Bachelor's with 15+
• 6+ years leadership experience

Preferred Qualifications

• PhD in statistical genetics or related computational/quantitative field
• 8+ years of relevant postdoctoral research and/or industry experience
• Experience leading efforts to apply genetics to drug discovery
• Deep expertise in statistical genetic methods (GWAS, exWAS, Mendelian Randomization, colocalization, polygenic risk scores)
• Advanced hands-on knowledge of R or Python
• Familiarity with functional genomics
• Managerial and mentorship experience in industry setting