Postdoctoral Researcher, Integrative Genetics and Multi-Omics Data Science

What you'll do

• Develop scalable computational pipelines to integrate genetics, proteomics, and electronic health record data.
• Create robust statistical methods for causal modeling using human genetics and perturbation-based sequencing datasets.
• Analyze biobank-scale datasets to identify candidate biomarker signals and disease associations.
• Identify causal gene–pathway–disease relationships to support target discovery across multiple therapeutic areas.
• Track and pilot innovative computational and statistical methods in the fields of statistical genetics and biology.
• Communicate research findings through peer-reviewed publications and internal presentations to cross-functional teams.

What we're looking for

• Must hold a PhD or be on track to receive one by spring 2027 in a quantitative field such as Statistics, Biology, or Computer Science.
• Demonstrated experience working with large-scale biobank or consortium phenotypic, genetic, and biomarker datasets.
• Experience analyzing large-scale omics datasets including WGS, transcriptomics, and proteomics.
• Hands-on experience applying or developing statistical genetics methods like GWAS, QTL mapping, and polygenic scores.
• Proficient programming skills in R and/or Python for data analysis and pipeline development.
• Experience with cloud platforms (e.g., AWS) or HPC environments including parallel computing and scalable workflow design.
• Excellent written and oral communication skills with a demonstrated ability to publish methodological papers or innovative applications.