Senior Expert Data Scientist, Human Genetics

Overview

In this role you advance genetically informed approaches to precision medicine by applying human genetics and genomic data science to CVM drug discovery. You will work with cross-functional teams to derive actionable insights from genetic data to identify targets and refine patient stratification. Your work supports innovative therapeutic programs and translational research, balancing rigorous analysis with clear scientific communication. This position offers the chance to shape target discovery and patient selection in a top pharmaceutical research environment.

Pay / Benefits

• salary range $126,000–$234,000
• performance-based cash incentive
• eligibility for annual equity awards
• comprehensive health, life, and disability benefits
• 401(k) with company match
• generous time off package

Responsibilities

• Analyze WES/WGS data to identify variants, genes, and pathways linked to CVM diseases
• Develop and apply statistical genetics and bioinformatics methods for annotation, association testing, burden testing, and causal inference
• Support identification, evaluation, and validation of novel therapeutic targets using genetic evidence and genomic data
• Contribute to patient selection and stratification via genetically defined subgroups
• Integrate genetics results with transcriptomic, proteomic, functional genomics, and clinical data to derive disease and target insights
• Collaborate with cross-functional teams to define analytical strategies and translate results into recommendations
• Develop reproducible, well-documented analytical workflows and contribute to best practices in genomic data analysis
• Communicate results through presentations, reports, manuscripts, and project updates
• Stay current with advances in genetics, genomic medicine, and computational methods relevant to drug discovery

Key requirements

• PhD with 2-3+ years or MS with 6+ years of industry experience in human genetics, statistical genetics, computational biology, bioinformatics, epidemiology, biostatistics, or a related field
• Postdoctoral experience preferred
• Demonstrated experience analyzing WES/WGS data including QC, annotation, filtering, association testing, and interpretation
• Strong understanding of human genetics and statistical genetics principles, including rare-variant analysis and genotype-phenotype associations
• Familiarity with GWAS, PheWAS, polygenic risk scores, Mendelian randomization, or functional genomics
• Experience integrating genomic data with clinical or multi-omic datasets
• Programming in Python or R; Linux/Unix computing; knowledge of workflow tools (Git) and reproducible research
• Ability to work with large-scale genomic datasets and derive meaningful biological conclusions
• Strong scientific communication, collaboration, and problem-solving skills
• Preferred: experience applying human genetic evidence to target discovery, target validation, or drug development

• strong communication
• collaboration
• problem-solving
• Python
• R
• Linux/Unix

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