This job is closed
Applications are no longer available for this announcement. Explore current related opportunities below.
Job description
Immunology Informatics / Human Genetics
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, AllofUs, FinnGen) 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 variants, 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 variants.
Ability to critically evaluate statistical/analytical pipelines for genetic analysis and communicate methodological trade-offs to interdisciplinary teams.
Experience in immunology/immune-mediated population genetics.
Experience integrating multi-omics with genetics data, including pQTL or eqTL analysis.
Familiarity with drug target identification/validation criteria and workflows in a pharmaceutical or biotech R&D setting.
Experience collaborating with external genetics/data partners or academic consortia.
What Success Looks Like in This Role
Providing a defensible, well-documented genetic evidence assessment that underpins target prioritization decisions across the immunology portfolio.
Proposing novel targets within the immunology space.
Ensuring that MR and other causal-inference methods are sound, appropriately caveated, and actionable for non-specialist stakeholders.
Establishing strong working relationships with target discovery, translational medicine, and external genetics collaborators.
Worksite address
indianapolis, IN, 46262, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.