Overview In this role you will conduct genetic and phenotypic analyses on large-scale datasets to discover and validate new therapeutic targets. You will work closely with pre-clinical, clinical, and R&D teams to translate genetic findings into actionable targets, leveraging multi-omics data and advanced statistics. You will implement data harmonization, prioritize variants, and drive end-to-end analytic studies that support Regeneron's therapeutic programs. The work blends in-house and public resources to accelerate target discovery at the Regeneron Genetics Center.
Compensation / Benefits- annual bonuses or incentive plans
- equity awards
- pension or retirement benefits
- 401(k) company match
- health and wellness programs
- paid time off
Responsibilities- conduct genetic association analyses (GWAS, EXWAS, rare variant analyses, Mendelian randomization, LD Score regression, polygenic risk scoring, pleiotropy analysis, meta-analysis)
- integrate genetic data with multi-omics datasets for target discovery and validation
- develop and implement data harmonization and normalization methods across resources
- apply functional genomic data to prioritize variants and genes
- perform QC on large-scale genetic and phenotypic datasets
- identify and interrogate data-driven hypotheses in analytic and translational genetics
- design and independently execute analytic studies from conception to completion
- collaborate with Regeneron R&D groups to advance therapeutic programs
Key requirements- PhD in a relevant field with 0-2 years of omics data and genetic association analysis experience
- Proficiency in multi-omic data integration for therapeutic target discovery
- Experience developing data harmonization/normalization methods
- Experience with GWAS, EXWAS, rare variant analyses, Mendelian randomization, LD Score regression, polygenic risk scores, pleiotropy analyses, meta-analysis
- Strong programming skills in Python, R, C/C++, Bash, and/or Julia
- Strong quantitative skills including regression, classification, Bayesian methods, hypothesis testing
- Experience with analytic and visualization tools (Git/Github, Claude, Adobe, Docker)
- Immunologic datasets or immunology experience preferred
- collaboration across cross-functional teams
- analytical rigor
- communication of complex results
- Python
- R
- C/C++
