Translational Data Scientist, Multi-Omics & Target Biology

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. The Human Genomics and Translational Data Sciences team within Cardiometabolic Research (CMR) Data Science is hiring a Translational Data Scientist to help build conviction around therapeutic targets using human data. You will work closely with CMR scientists - geneticists, biologists, and translational leads - to interrogate genomics, proteomics, transcriptomics, and other omics layers, and to turn that evidence into a clear, defensible view of whether a target is worth pursuing and how it should be pursued. This is a science-forward analytical role. You will employ human genetics (biobanks and internal data) to tell us where to look and multi-omics to better annotate what is in relevant tissues, cells, and patients. You will apply tools for, and integrate results from, GWAS/RVAS, PheWAS, and post-GWAS workflows. You will integrate proteomics, bulk and single-cell transcriptomic data, and functional annotation to characterize targets of interest, including direction of effect, tissue and cell-type context, likely mechanism, biomarker potential, and safety liabilities inferred from human variation. You will be expected to bring strong quantitative and statistical instincts, genuine biological curiosity, and the ability to work directly with bench and translational scientists - framing the right question, choosing a defensible analysis, and communicating what the data does and does not support. You will present and advocate for emerging targets with senior leadership. Modern tooling, including LLM-assisted and agentic workflows, is part of how we work; comfort using it to move faster is welcome. Key Responsibilities Target Conviction and Due Diligence Partner with CMR scientists to assemble and critically appraise the human evidence for targets of interest - genetic association, direction of effect, allelic series, and phenotypic consequence - and translate it into a clear conviction narrative Work alongside bench scientists to integrate human evidence with in vitro and in vivo findings Run and interpret target due-diligence analyses across large human cohorts and public resources (UK Biobank, All of Us, proteogenomic studies), including fine-mapping, colocalization, Mendelian randomization, PheWAS, and proteomics analyses Use human genetic variation to anticipate on-target safety liabilities and to identify indication-expansion and patient-stratification opportunities Produce standardized, reproducible target assessments that stand up to scrutiny in project, portfolio, and governance forums Bring an independent, evidence-led point of view, including a clear articulation of what the data does not support, and what would be needed to resolve it Multi-Omics Integration and Target Characterization Analyze and integrate proteomics, bulk and single-cell transcriptomics as needed to help build conviction Build integrative analyses that connect genetic evidence to molecular readouts - pQTL and eQTL mapping, protein-phenotype associations, and pathway or network context Develop and test mechanistic hypotheses with CMR scientists, and help design the human and preclinical datasets needed to discriminate between them Support biomarker and translational readout selection for preclinical studies and early clinical development, including target engagement and pharmacodynamic markers With data engineers, contribute to building reusable analysis workflows and clear visualizations so target evidence can be revisited, extended, and reused across projects Creatively use modern tooling, including LLM-assisted and agentic workflows, where it speeds up routine evidence gathering and reporting Collaboration Across Lilly Research Labs Embed with CMR target teams - shaping analytical plans, attending project meetings, and presenting findings to biology, translational, and clinical audiences Partner closely with statistical geneticists, computational biologists, and data engineers within CMR Data Science and across other Lilly Research Labs teams Coordinate with platform and data engineering groups to access, harmonize, and scale the internal and external omics datasets your analyses depend on Contribute to internal knowledge sharing - analysis reviews, demos, documentation, and helping colleagues get unblocked Drive publication of insights from our internal data and clinical trials, showcasing the science to the wider community Basic Requirements Ph.D. in human genetics, computational biology, bioinformatics, systems biology, statistics, or a related quantitative field with 1+ years post-Ph.D. experience; or M.S. with 4+ years of relevant experience analyzing human omics data. Candidates with more experience are encouraged to apply. Demonstrated experience analyzing and interpreting human genomics data - GWAS summary statistics, rare-variant and burden results, fine-mapping, colocalization, or Mendelian randomization Preferred Qualifications Hands-on experience with at least one additional omics modality at scale (proteomics, bulk or single-cell transcriptomics, or epigenomics) and with integrating evidence across modalities Strong programming skills in Python and/or R, including version control (Git), reproducible analysis practices, and clear documentation of methods and assumptions Solid grounding in applied statistics - regression modeling, multiple-testing correction, confounding, causal inference concepts, and honest treatment of uncertainty Working familiarity with common bioinformatics formats and tools (VCF, BED, GTF, BAM; PLINK, REGENIE, bcftools, or similar) and with large-scale human cohort resources (UK Biobank, All of Us, gnomAD, GTEx, Open Targets) Demonstrated ability to work directly with biologists and translational scientists - framing tractable questions, choosing defensible analyses, and communicating results to non-computational audiences Strong biological literacy to reason about target mechanism, tissue and cell-type relevance, and pathway context; genuine interest in cardiometabolic disease biology Comfort with cloud computing environments (AWS, GCP, or Azure) and Linux/command-line work A collaborative, low-ego attitude and the ability to work successfully in a matrixed environment Experience with applied proteogenomics - pQTL analysis, plasma proteomic platforms (Olink, SomaScan), and the underlying statistical genetics concepts Experience with single-cell or spatial transcriptomics, including cell-type deconvolution and cross-tissue integration Track record of contributing to target identification, validation, or due-diligence decisions in a drug discovery setting Familiarity with preclinical model data and with translational biomarker and pharmacodynamic readout development Experience with relational and/or graph databases, and with biomedical ontologies Hands-on experience with modern AI tooling - LLM APIs, agentic workflows, or MCP