Sr Statistical Geneticist

Tarrytown, NY, United States
Nov 26, 2020
Required Education
Position Type
Full time
The Regeneron Genetics (RGC) has built one of the largest databases of genetic sequence from hundreds of thousands of participants linked to rich phenotype data from electronic health records and other sources. We leverage these data to gain insights into disease mechanisms, identify novel therapeutic targets, and enable Regeneron to deliver better medicine to help patients in need. We are looking to hire an enthusiastic and motivated Statistical Geneticist to join the results mining group within the Analytical Genomics and Data Science Team.

In this role, a typical day may include:
• Critical review of associations between genetic markers and human phenotypes, to ensure they are accurate and reliable.
• Developing research workflows to identify and prioritize potential therapeutic targets from databases of billions of genetic associations. This includes both internal and public resources which enable insights about gene function.
• Interpreting the effect loss-of-function variants on human disease and complex traits.
• Creating novel data summaries and visualizations which distill "big data" ideas into interpretable and actionable insights for a broad audience.

You might be right for this role if you have:
• Experience with genome-wide association analysis using imputed or exome sequence data.
• The ability to bring innovative thinking to drug target discovery and novel indications.
• A strong work ethic and ability to drive projects forward.
• A desire to work in a cross-functional team environment.

To be considered for this role, you must have a A PhD in statistical genetics, computational biology, bioinformatics or a related field with 0 - 2 years of relevant experience, or a master's degree with 2 years of relevant experience; Experience with genetic association tools (PLINK, SAIGE, METAL, regenie) and knowledge of bioinformatics databases supporting genetically-driven drug discovery programs (GTEx, GWAS catalog); Proficiency in at least one scripting language (Python, R, C/C++), command-line interfaces and cloud computing platforms. Experience with interactive data visualization methods and tools is desirable; Familiarity with human genetic concepts, DNA sequence analysis, and related areas such as transcriptomics, rare human disease or model organism databases; Outstanding communication skills and an ability present to statistical geneticists, computational biologists, and therapeutic area specialists.