Statistical Geneticist: Immunology Target Discovery

American Society of Gene & Cell Therapy; Indianapolis, Northern (IN, KY); Hybrid; USD 153,000 - 246,000; Full time

Job summary

Lilly seeks a seasoned statistical geneticist to strengthen target identification by applying Mendelian randomization, GWAS interpretation, and multi-omic integration across the immunology pipeline. You will collaborate with target discovery, translational medicine, and bioinformatics teams to prioritize therapeutic targets with strong human causal evidence.

The role sits at the interface of human genetics, translational biology, and drug development strategy, with leadership in evaluating

Qualifications

• Ph.D. in statistical genetics, population genetics, genetic epidemiology, biostatistics, computational biology, or a related quantitative field.
• Demonstrated experience working on large-scale population datasets and causal inference from human genetic datasets.
• Strong proficiency in R, ideally with tidyverse workflows; comfort with version control (Git).

Responsibilities

• Lead systematic genetic evidence reviews for immunology drug targets using GWAS, exome/genome sequencing, and rare-variant data.
• Build and maintain target evaluation frameworks that score genetic evidence and development-stage relevance.
• Interrogate data from Open Targets, GWAS Catalog, UK Biobank, FinnGen to support go/no-go decisions.
• Propose novel drug targets with strong human causal evidence for autoimmune diseases.
• Communicate genetic and statistical findings clearly to interdisciplinary teams.

Skills

Mendelian randomization

GWAS interpretation

Causal inference

Data synthesis

Education

Ph.D. in statistical genetics or related field

Tools

R (tidyverse)

Git

PLINK

Job description

Lilly seeks a seasoned statistical geneticist to strengthen target identification by applying Mendelian randomization, GWAS interpretation, and multi-omic integration across the immunology pipeline. You will collaborate with target discovery, translational medicine, and bioinformatics teams to prioritize therapeutic targets with strong human causal evidence.

The role sits at the interface of human genetics, translational biology, and drug development strategy, with leadership in evaluating