Post Doctoral Scientist – Human Genomics and Translational Data Sciences

Role Summary

The postdoctoral scientist position will advance precision medicine by applying statistical genetics to human genomics and translational data sciences within Lilly's Cardiometabolic Research team. You will integrate and analyze whole genome sequencing, proteomics, metabolomics, and phenotypic data from biobank and population datasets on cloud-based multi-omics platforms to identify genetic and molecular factors influencing disease risk and treatment response. You will collaborate with cross-functional teams in DSCB, early discovery, and clinical groups to guide therapeutic development.

Location:

Indianapolis, Indiana.

• Apply statistical and computational approaches to analyze WGS/WES, proteomics, metabolomics, and clinical data for biomarker discovery.
• Conduct rigorous analyses of large-scale population cohorts and biobank datasets to identify genetic variants and causal genes associated with disease outcomes.
• Develop and implement machine learning and bioinformatics pipelines to integrate multi-omics data.
• Collaborate with interdisciplinary teams, including geneticists, epidemiologists, and clinicians, to interpret findings and guide therapeutic development.
• Prepare scientific reports, presentations, and publications detailing research outcomes.
• Contribute to the development of novel statistical methods for analyzing high-dimensional biological data.

• Authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.

• PhD in statistical genetics, bioinformatics, computational biology, biostatistics, or a related quantitative field.

• Expertise in whole genome and whole exome sequencing analysis, proteomics, metabolomics and other molecular data analysis, and clinical outcomes research.
• Strong proficiency in statistical modeling, machine learning, and high-dimensional data analysis.
• Experience working with large biobank and cohort datasets (e.g., UK Biobank, All of Us, Finn Gen).
• Proficiency in programming languages such as R, Python, and SQL for data analysis.
• Familiarity with genetic association studies, GWAS, and polygenic risk scores.
• Excellent communication and collaboration skills to work effectively in cross-functional teams.
• Experience in pharmaceutical or biotech industry settings.
• Knowledge of functional genomics and multi-omics data integration.
• Strong publication record demonstrating contributions to statistical genetics and biomarker discovery and analysis.
• Prior experience in cardiometabolic research.
• Prior experience with polygenic risk score models.