Director, Statistical Genetics

Director, Statistical Genetics

When you join BMS, you are joining a high-achieving team united by a common mission.

The Informatics and Predictive Sciences (IPS) mission is to Pioneer, Partner and Predict to drive transformative insights for patient benefit. IPS conducts applied computational research in areas that include genomic, structural and molecular informatics, computational and systems biology, patient selection and translational biomarker research, and broader fields including knowledge science, epidemiology and machine learning, across the full lifecycle of drug discovery and development and across all therapeutic areas  do this in close partnership with scientific and clinical experts in the field, both inside and outside the company.

We perform innovative science to empower key data-driven decisions across a rich pipeline of next-generation medicines. In doing so, our work transforms the lives of patients, as well as our own lives and careers.

Here, you’ll get the chance to grow and thrive through opportunities that are uncommon in scale and scope. You’ll pursue innovative ideas while advancing professionally alongside some of the brightest minds in biopharma.

We seek a talented statistical genetics expert to 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. This role will sit within Informatics & Predictive Sciences, a globally distributed group driving innovative computational research for discovery and early development within BMS research.

A key focus will be aligning with disease area strategy to impact drug discovery and translational efforts using insights from causal human biology. This will include 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. It will also include synthesizing and communicating key findings at cross-functional meetings.

This position offers an exciting opportunity to impact human health through innovative human genomics research. It is also an opportunity to work closely with the broader scientific community through pre‑competitive collaborations (e.g. Finn Gen, UK Biobank, UK Genes & Health, Alliance for Genomic Discovery), and to publish and present industry‑leading work in this area.

Location:

Cambridge, MA

• Drive design and implementation of computational strategy to infer causal mechanisms driving disease using human genetics and other data types (e.g. Mendelian randomization with proteomics, TWAS, colocalization)
• Lead team to perform cross‑biobank analyses focused on identifying mechanisms underlying human genetic risk factors (e.g. 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 that will enable deep understanding of 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 in multiple settings (including 1:1, seminars, project meetings, and external publications)

• Bachelor’s Degree with 15+ years of academic / industry experience
• Or Master’s Degree with 12+ years of academic / industry experience
• Or PhD with 8+ years of academic / industry experience
• 6+ years leadership experience

• PhD in statistical genetics or a related computational/quantitative discipline field with
• 8+ or more years of relevant postdoctoral research and/or industry experience preferred
• Experience in leading efforts to apply genetics to drug discovery
• Deep scientific expertise in application of statistical genetic methods (GWAS, exWAS, Mendelian Randomizaiton, colocalization, polygenic risk scores)
• Advanced hands‑on knowledge of at least one high‑level programming…