Job Summary and Essential Functions:
The Regeneron Genetics Center is a wholly-owned subsidiary of the Company organized to collaborate with health systems and research groups to elucidate, on a large scale, genetic factors that cause or influence a range of human diseases. Building upon Regeneron's strengths in mouse genetics and genetics-driven drug discovery and development, the Center will specialize in ultra-high-throughput exome sequencing and computational biology; discovery of genotype-phenotype associations through linkage to well-annotated de-identified patient electronic medical records; and validation of discoveries using Regeneron’s VelociGene® technology. Our interests encompass a breadth of different areas such as Mendelian and family frameworks, large-scale population genetics (both common and rare variants), and gene-gene interactions. Program goals include target discovery, indication discovery, and patient-disease stratification. Objectives include advancing basic science around the world through public sharing of discoveries, providing clinically-valuable insights to physicians and patients of collaborating health-care systems, and identifying novel targets for drug development.
We are looking for a Bioinformatics R&D Manager to lead our NGS bioinformatics research and development efforts, focusing on development of novel analysis tools and methods for large-scale exome data analysis. Analysis challenges include developing analysis tools to help understand complex and repetitious genomic regions, development of tools to produce and improve variant calls for complex variants, mining proprietary human genetic variation data and associated medical information to uncover gene-disease links and new targets, new indications for existing drug targets, and new ways to stratify disease-bearing populations.
• Develop new tools and methods to understand the genome, and identify variations in novel, interesting, and/or complex regions.
• Collaborate with scientists from Human Genetics, Pre-clinical, and Clinical departments to design tools and methods to support experiments linking variation to medical information.
• Communicate results with scientists, refine analysis and tools based on feedback, and plan/prioritize follow-up experiments.
• Prepare clear, concise and easy-to-understand presentations and documentation for collaborators, senior management and regulatory agencies.
• Analyze proprietary and public genetics data to support identification of new drug targets, suggest new indications for existing drug therapeutics, support clinical studies, and suggest new disease stratification strategies.
• Participate in team effort of organizing and integrating proprietary and public genetics data to enable data sharing and visualization for bench scientists.
• Contribute to team effort of developing genetics analytical strategies and establishing data analysis best practices.