Manager, Translational Genetics (Therapeutic Area Genetics)

Role Summary

Manager, Translational Genetics (Therapeutic Area Genetics) overseeing Translational Genetics software development to analyze and interpret large-scale genomic data and translate findings into biological insights for Regeneron.

Responsibilities

• Architect elegant solutions and design robust, reusable functions and packages that abstract complex genetic analyses into scalable tools; transform analyses into production-grade R packages used across the team.
• Mine large sequencing datasets to uncover biological insights that drive drug development in cardiometabolic disease.
• Optimize algorithms for performance at scale, refactor legacy code, and implement modern software engineering practices (version control, CI/CD, containerization) in a scientific context.
• Debug issues, write comprehensive documentation, and provide technical mentorship to tool users.
• Collaborate with geneticists, clinicians, and bioinformaticians to translate analytical needs into software solutions and co-develop novel analysis strategies.
• Stay at the forefront of statistical genetics, applying latest GWAS and post-GWAS methods, integrating diverse data types, and generating testable hypotheses about human disease.

Qualifications

• Masters or PhD in Bioinformatics or Statistical Genetics with a heavy computational component, or degree in Computer Science, Applied Mathematics, Physics, or other quantitative field with specialization in genetics.
• Hands-on experience with statistical genetics, GWAS and post-GWAS.
• Strong expertise in writing efficient scientific code in R, Python, or other languages.
• Experience developing scientific packages and/or web applications.
• Experience developing and deploying in a cloud infrastructure (e.g., AWS, GCP, DNAnexus) using CI/CD and containerization (Docker).
• Familiarity with standard bioinformatics tools (e.g., PLINK2, HTSlib, tabix) and data formats (VCF, BED, BGEN).
• Experience with Linux/UNIX command line and SQL databases.

Skills

• Quantitative analysis and coding best practices; ability to write clean, maintainable code.
• Proficiency in statistical genetics concepts such as polygenic scores, LD, GWAS, and post-GWAS analyses.
• Ability to design composable, reusable software solutions and avoid duplication.
• Collaborative mindset to work with multidisciplinary teams and translate analytical needs into software tools.