Senior Scientist, Neuropsych Genetics

Role Summary

We seek a talented statistical geneticist to design, implement, and oversee discovery and translational efforts for neuropsychiatric diseases using large-scale human genetic data. This role sits 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 company strategy for neuropsychiatric diseases to impact drug discovery using insights from causal human biology. This will include leading efforts to apply innovative statistical genetics methods to build and inform therapeutic hypotheses, identify indications and patient populations for programs, and analysis of clinical trial data for reverse translation. It will also include synthesizing and communicating key findings at cross-functional meetings. Location Cambridge, MA

Responsibilities

• Coordinate cross-biobank analyses focused on identifying human genetic risk factors for neuropsychiatric diseases (e.g. GWAS meta-analysis, rare variant burden tests)
• Design and perform integrative analyses to infer causal mechanisms driving disease using human genetics and other data types (e.g. Mendelian randomization with proteomics, colocalization, integration with scRNAseq)
• Evaluate and prioritize multi-modal, disease-specific datasets that will enable deep understanding of causal human biology of neuropsychiatric disease
• 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)

Qualifications

• Required: Bachelor's Degree with 12+ years of academic / industry exeperience
• Required: Or Master's Degree with 10+ years of academic / industry experience
• Required: Or PhD with 8+ years of academic / industry experience
• Preferred: PhD in statistical genetics, neurogenetics, or a related computational/quantitative discipline field with 4+ or more years of relevant postdoctoral research and/or industry experience preferred
• Preferred: Experience in leading efforts to apply genetics to drug discovery
• Preferred: Deep scientific expertise in application of statistical genetic methods (GWAS, exWAS, Mendelian Randomization, colocalization, polygenic risk scores)
• Preferred: Advanced hands-on knowledge of at least one high-level programming language such as R or Python
• Preferred: Familiarity with functional genomics preferred
• Preferred: Demonstrated ability to advance multi-disciplinary team projects required