Associate Manager, Statistical Machine Learning

The Regeneron Genetics Center (RGC) is a wholly-owned subsidiary of the Company, whose goals are to apply large scale human genetics to identify new drug targets and to guide the development of therapeutics programs and precision medicine. Building upon Regeneron's strengths in mouse genetics and genetics-driven drug discovery and development, the RGC specializes in ultra-high-throughput exome sequencing, large scale informatics and data analysis encompassing genomics and electronic health records, and translating genetic discoveries into new biology and drug discovery opportunities. The RGC leverages multiple approaches including large population based studies, Mendelian genetics and family based studies, founder population genetics, and large-scale disease focused projects and has developed a network of over 50 collaborations with research organizations around the world. Including some of the largest sequencing studies in the world, such as the DiscovEHR study in collaboration with Geisinger Health System, and an initiative to sequence 500,000 participants with the UK Biobank, the RGC has built one of the largest human genetics databases, including sequence data from over several hundred thousand participants and rapidly growing. Our interests encompass a breadth of different areas across all therapeutic areas and the RGC is highly integrated into all facets of research and development at Regeneron. 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 providers of collaborating health-care systems, improving patient outcomes, and identifying novel targets for drug development.

Summary
We are looking for a scientist with postdoctoral experience or greater with machine learning expertise who will play a central role in research and development of machine learning approaches to the analysis and interpretation of 100,000s of genotyped and sequenced humans, with the goal of generating the knowledge that will enable Regeneron to deliver better medicines to the patients who need them. The scale and breadth of the data being collected at the RGC provides opportunities for research on a wide range of machine learning topics. Work collaboratively with teams that combine expertise in machine learning, genomics, biology, computation and medicine to design, execute and refine analysis that connect genetic variation to human diseases and health.

Responsibilities

• Develop novel machine learning methods applicable to the large scale genetic, electronic health record and imaging data-sets being collected at the RGC and Regeneron.

• Contribute to teams of investigators executing cutting edge genomic analysis, both within an institution and across institutions.

• Build machine learning solutions at the interface of statistical genetics and clinical care and/or direct-to-consumer genetics.

• Implement solutions using modern cluster and cloud computing environments is required. The candidate will routinely use advanced tools for genomic analysis, for statistical analysis and computation to execute analysis at scale and to facilitate effective annotation, sharing and collaboration.

• Critically review and provide feedback on analysis plans, results and summaries that involve machine learning solutions to ensure they are accurate and reliable. Identify potential problems and propose remedies or refinements.

• Summarize and present the results of machine learning approaches applied to human genetic studies to a variety of technical audiences, ranging from experts in statistical genetics and computation to experts in biology, drug design and medicine.

• Work in a highly interactive environment with a diverse team of colleagues. Provide mentorship and guidance to more junior colleagues to help them develop their full potential and build new skills and abilities. The outstanding candidate will be able help teams of skilled individuals to consistently achieve high levels of motivation, enthusiasm and performance.

Requirements

• A strong track record in development of machine learning methods, evidenced by publications.

• Broad understanding and practical experience of the existing methods in machine learning - deep learning, transfer learning, kernel methods, Bayesian statistics, supervised and unsupervised learning, dimension reduction and model selection.

• An outstanding candidate will have knowledge and practical experience of genetic association and population genetic studies - ranging from design, quality control, association analysis, phasing, imputation, polygenic risk scores, heritability, population structure, functional interpretation, data visualization, and follow-up experiments in cells and model organisms.

• Experience working in teams with diverse skills, and of mentoring and developing talent.

• Ability to summarize and distill results concisely. Excellent oral presentation and writing skills.

• Skills in a range of programming languages suitable for at scale analysis (e.g. C/C++, java, python or R) as well as relevant libraries and frameworks (TensorFlow, Eigen, scikit-learn, MLlib, SQL, jQuery).

• Expertise in developing analysis packages and tools is highly desired, as demonstrated by leadership or contributor roles to code available on GitHub or elsewhere.

• Minimum Years of Experience: 2-5 years of experience post PhD expected.

This is an opportunity to join our select team that is already leading the way in the Pharmaceutical/Biotech industry. Apply today and learn more about Regeneron's unwavering commitment to combining good science & good business.

To all agencies: Please, no phone calls or emails to any employee of Regeneron about this opening. All resumes submitted by search firms/employment agencies to any employee at Regeneron via-email, the internet or in any form and/or method will be deemed the sole property of Regeneron, unless such search firms/employment agencies were engaged by Regeneron for this position and a valid agreement with Regeneron is in place. In the event a candidate who was submitted outside of the Regeneron agency engagement process is hired, no fee or payment of any kind will be paid.

Regeneron is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability status, protected veteran status, or any other characteristic protected by law.