At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

The Diabetes, Obesity and Complications Therapeutic Area (DOCTA) focuses on new therapeutic approaches for the treatment of diabetes, obesity and cardiometabolic diseases. Starting from an idea, we work with partners across Lilly to discover and develop novel biologic, small molecule and nucleic acid-based therapeutics. Our focus is the patient: by understanding the biology and pathophysiology underlying disease states, we aim to address the root cause of disease, and develop breakthrough therapies. We have one of the strongest pipelines in the industry and a track record of delivering impactful medicines that improve people’s lives.

In this hands-on role, the Statistical Geneticist will use internal and external human genetic data to derive scientific insights and drive data-driven decision-making within the organization. The successful candidate will collaborate with computational biologists, platform architects, and bioinformaticians across the Data Sciences and Computational Biology (DSCB) group within DOCTA and the broader Lilly research environment. Their goals will include identifying genetically-based disease targets, finding potential expanded clinical indications for existing assets, classifying and validating patient subpopulations, and understanding disease mechanisms. This role is an exciting opportunity to be at the forefront of scientific exploration in a dynamic research field. You can build a career with a company committed to tackling obesity, diabetes, and cardiometabolic diseases.

Interested in working on an innovative team focusing on new therapeutic approaches? Apply today!

Key Responsibilities:Collaborate with software engineers and platform architects. Develop auditable pipelines for efficient genetic data analysis. This includes WGS, WES, and genotyping studies from internal and external sources.Develop and use cloud-based pipelines for annotation of variants according to ACMG criteria (population frequency, computational scores, clinvar, and literature-based functional, pedigree, and statistical data, etc)Design and implement genetic analyses from multiple data sources. This includes standard association analyses, rare variant analysis, and polygenic risk score analysis. Apply other relevant methods as needed.Perform post-computational analyses to interpret findings within biological and clinical contextCollaborate with computational biologists, translational researchers, and clinical scientists. Validate identified genetic targets. Perform genetic analyses of targets identified by other research groups.Interpret and clearly communicate results from genetic analyses, including development of scientific manuscripts, posters, and presentationsEngage in code and documentation review within the team and across other teams within the DSCB teamAdhere to industry-standard standard methodologies for scientific project documentationKey Requirements:PhD or equivalent in Statistical Genetics, Genetic Epidemiology, Population Genetics, or related field0-3+ years post-PhD experienceAdditional Skills/Preferences:Demonstrated track record performing end-to-end analysis of human genetic data, including experimental design, execution, and biological interpretation requiredAbility to work with multiple genetic data formats (VCF, BAM/CRAM, BED, etc) and prior experience with variant annotation pipelines (SNPeff, ANNOVAR, Varsome, etc) requiredExpertise in one or more programming languages such as R, Python, etc. requiredExpertise in a metabolism-related field such as obesity, diabetes, MASH, cardiometabolic, and/or cardiorenal strongly preferredPrior experience performing complex analyses in cloud-based environments preferred; prior experience with DNANexus a plusPrior experience working with additional data formats, including RNA-seq, metabolomic, and proteomic data preferredPrior experience working with clinical data preferredAbility to prioritize and manage multiple competing priorities within a fast-paced environment requiredThe ability to communicate complex scientific and computational concepts to non-computational and non-scientist audiences requiredAbility to represent the DOCTA DSCB team internally and externallyStrongly team-oriented with a customer focused design thinking approach

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