Manager, Analytical Genetics and Data Science

Regeneron Pharmaceuticals•Tarrytown, NY

63d

About The Position

We are seeking a highly motivated and talented Manager, Analytical Genetics and Data Science to join the AGDS team at the Regeneron Genetics Center (RGC) to help pioneer the analysis of large-scale proteomic datasets. In this role, you will develop and apply proteomic-based predictive models at scale, with a special emphasis on aging and-age-related diseases. Additional focus areas include the integration of multi-omic datasets, methods development for improved data harmonization and portability, and therapeutic target identification in collaboration with other RGC teams. In this role, a typical day might include the following: Plan, develop, and execute large-scale analyses of proteomic datasets, with an emphasis on aging and age-related diseases. Utilize machine learning techniques to build predictive models and generate insights from multi-omic datasets. Develop and implement methods for data harmonization and normalization across distinct cohorts to ensure consistency and reproducibility of results. Integrate proteomic, genomic, and other multi-omic data to improve therapeutic target discovery and prioritization. Lead the development of reproducible workflows and pipelines for multi-omic data analysis. Collaborate with cross-functional teams to drive large-scale omics projects and support translational research goals. Stay abreast of emerging trends in proteomics, machine learning, and multi-omics to continuously enhance analytical strategies.

Requirements

A PhD, MD, or MD/PhD in a relevant field (e.g., bioinformatics, computational biology, genetics, or related disciplines).
At least 3 years of post-PhD experience in analyzing large-scale omics datasets, with a focus on proteomics.

Nice To Haves

Proficiency in Python and R, with familiarity in workflow languages such as WDL.
Demonstrated expertise in machine learning and predictive analytics applied to biological data.
Strong understanding of multi-omic data integration and its application in therapeutic target discovery.
Experience in developing and implementing methods for data harmonization and normalization.
Proven ability to independently lead and manage research projects from conception to completion.
Excellent communication and collaboration skills, with a track record of working effectively in interdisciplinary teams.

Responsibilities

Plan, develop, and execute large-scale analyses of proteomic datasets, with an emphasis on aging and age-related diseases.
Utilize machine learning techniques to build predictive models and generate insights from multi-omic datasets.
Develop and implement methods for data harmonization and normalization across distinct cohorts to ensure consistency and reproducibility of results.
Integrate proteomic, genomic, and other multi-omic data to improve therapeutic target discovery and prioritization.
Lead the development of reproducible workflows and pipelines for multi-omic data analysis.
Collaborate with cross-functional teams to drive large-scale omics projects and support translational research goals.
Stay abreast of emerging trends in proteomics, machine learning, and multi-omics to continuously enhance analytical strategies.