Statistical​/Population Geneticist - Immunology Research

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.

** Function:
** Immunology Informatics / Human Genetics

** Level:
** R5-R6

*
* Location:

** Indy or Boston

About the Role

The Immunology Informatics team integrates human genetics into drug discovery and development across the autoimmune disease spectrum (rheumatology, gastroenterology, and dermatology). We are seeking a statistical or population geneticist to strengthen our target identification and validation efforts by applying rigorous quantitative genetics methods - including Mendelian randomization, GWAS interpretation, fine mapping co-localization, and multi-omic integration - to identify, prioritize and de-risk therapeutic targets across the immunology pipeline.

This role sits at the interface of human genetics, translational biology, and drug development strategy, working closely with target discovery, translational medicine, statistical, and bioinformatics colleagues, as well as external partners.

** What You'll Do*
* ** _Target Evaluation & Genetic Evidence Assessment_*
* + Lead systematic genetic evidence reviews for existing and emerging immunology drug targets, synthesizing GWAS, exome/whole-genome sequencing, and rare-variant data to assess causal support and directionality (loss-of-function vs. gain-of-function phenotype concordance with therapeutic hypothesis).

+ Build and maintain target evaluation frameworks that score genetic evidence quality, effect direction, and development-stage relevance across a target portfolio.

+ Interrogate and interpret summary and individual levels data from public and licensed genetic resources (e.g., Open Targets, GWAS Catalog, UK Biobank, Allof Us, Finn Gen) to support go/no-go and prioritization decisions for drug targets

+ Propose novel drug targets with strong human causal evidence for autoimmune diseases.

+ Make decision enabling judgements on drug target quality strong scientific rationale

** _Population Genetics, Mendelian Randomization, Causal Inference_*
* + Design, execute, and critically evaluate evidence from large population-scale datasets using Mendelian Randomization and other methods to test causal relationships between genetic variamts, biomarkers/proteins and immune-mediated disease outcomes.

+ Assess genetic instrument validity for Mendelian Randomization, pleiotropy, and sensitivity of MR findings (e.g., MR-Egger, weighted median, colocalization) and communicate limitations clearly to immunologists.

+ Collaborate with external genetics partners and academic collaborators on specific genetics programs.

** _Cross-Functional Collaboration & Communication_*
* + Translate complex genetic and statistical findings into clear, decision-relevant summaries for target discovery teams, translational scientists, and portfolio governance.

+ Contribute genetics-informed input to target nomination packages, competitive intelligence, and program strategy documents.

+ Represent statistical genetics perspective in cross-functional target review forums.

** Required Qualifications*
* + Ph.D. in statistical genetics, population genetics, genetic epidemiology, biostatistics, computational biology, or a related quantitative field (or M.D, M.S. with equivalent industry experience).

+ Demonstrated experience working on large-scale population datasets, Mendelian randomization (MR) methodology and causal inference from human genetic datasets

+ Strong proficiency in R, ideally within a tidyverse-based workflow; comfort with reproducible, version-controlled analysis (Git); basic knowledge of PLINK or other tools to manipulate large genotype datasets

** Preferred Qualifications*
* + Practical experience interrogating large-scale genetics/genomics databases (e.g. UK Biobank, AllofUS, Open Targets,) and their APIs.

+

Demonstrated proficiency using fine-mapping, and colocalization approaches for GWAS summary statistics to refine association signals and identify causal genetic…