Manager, Statistical Genetics

This individual contributor role is responsible for conducting analyses of large-scale human genetic and phenotypic datasets for the discovery and validation of new therapeutic targets at the Regeneron Genetics Center (RGC). Work draws on both in-house and public data resources, integrating genetic data with multi-omics datasets and applying state-of-the-art statistical methodologies across analytic and translational genetics. The role collaborates closely with pre-clinical and clinical development teams colleagues across Regeneron’s R&D functions.

In this role, a typical day might include the following:

• Conduct genetic association analyses using large-scale human genetic data, including genome-wide association analysis (GWAS), exome-wide association analysis (EXWAS), rare variant analysis, Mendelian randomization, LD Score regression, polygenic risk score modeling, pleiotropy analysis, and meta-analysis.

• Integrate genetic data with multi-omic datasets to support therapeutic target discovery and validation.

• Develop and implement methods for data harmonization and normalization across in-house and public data resources.

• Apply functional genomic data to prioritize variants and genes of interest.

• Perform quality control on large-scale genetic and phenotypic datasets.

• Identify and interrogate data-driven hypotheses as part of analytic and translational genetics work.

• Design and independently execute analytic studies from conception to completion.

• Collaborate with groups across Regeneron’s R&D functions, including pre-clinical and clinical development teams, to advance therapeutic programs.

This role might be for you if you have:

• Experience developing and implementing data harmonization and normalization methods

• Experience with genetic analysis approaches including GWAS, EXWAS, rare variant analysis, Mendelian randomization, LD Score regression, polygenic risk score modeling, pleiotropy analysis, meta-analysis, and use of functional data to prioritize variants and genes

• Ability to independently lead and manage research projects from conception to completion

• Strong communication and collaboration skills; proven track record of effective work in interdisciplinary teams

• Outstanding ability to summarize and present human genetic study results to diverse technical audiences, from statistical genetics specialists to biologists, drug designers, and clinicians

• Strong programming skills in languages such as Python, R, C/C++, Bash, and/or Julia.

• Strong quantitative skills, including applied statistical techniques such as regression and classification, Bayesian inference, and hypothesis testing. Experience with analytic and visualization tools including Git/Github, Claude, Adobe, Docker

To be considered for this role, we require:

• PhD with at least 3+ years of relevant experience

• Expertise in genetic association analyses using large-scale genetic data

• Proficiency in multi-omic data integration for therapeutic target discovery

• Experience working with immunologic datasets and/or experience with immunology is preferred