Research & Science

23andMe   •  

Mountain View, CA

Industry: Pharmaceuticals & Biotech


5 - 7 years

Posted 55 days ago

Who you are

We are looking for a colleague who will provide leadership and direction on the use and understanding of different computational approaches to facilitate the interpretation of GWAS hits and their likely role in traits and diseases. Familiarity with RNA-seq, eQTLs, functional annotations are essential. You should have the ability to communicate well, manage projects and teams, and function in a highly interactive scientific discovery environment. One of your primary functions will be to support our therapeutic discovery efforts and some experience in pharma would be an asset.

Our team is very collaborative, and the ability to communicate ideas and results to other scientists and non-scientists in the context of business objectives is key. In addition, candidates should have multiple years of experience leading development and implementation of novel methods and bioinformatics tools; executing independently developed ideas; a demonstrated ability to clearly and effectively communicate with a wide range of internal and external audiences; and experience building, mentoring, and leading a team of scientists.

The scientific questions we are working on, and the scope of our vision mean that most of the necessary tools and methods have yet to be developed anywhere in the world. We encourage presentation and publication of research results. The full list of our publications can be seen at

Essential Qualifications:

  • A PhD in Statistics, Computational Biology, Computer Science or a related field with at least six years of experience post-PhD.
  • A strong record of achievement, either through publications or successful industry achievements.
  • Experience managing teams and individuals.
  • Ability to communicate clearly and effectively.
  • Broad understanding and familiarity with functional genomics.

Additional assets:

  • Ability and experience in applying machine learning/AI approaches to genomic data.
  • Understanding and expertise in human genetics.
  • Understanding and expertise in design of experiments and analysis of complex data.