Scientist I/II, Statistical Geneticist at 23andMe in Palo Alto (HQ)

Summary: 23andMe is at the forefront of using human genetics to advance biomedical research and transform healthcare. We are looking for a talented research scientist in statistical genetics to join our highly productive research team, where they will apply scientific expertise towards creating insights from human genetic data for various applications. Responsibilities: - Model phenotypes of interest to derive genetic insights and therapeutic hypotheses - Analyze genetic variants associated with specific inherited traits, and across multiple traits of interest - Integrate human genetics and genomics datasets leveraging machine learning and statistical modeling techniques - Develop or apply methodology for the statistical analyses of large-scale genomic and phenotypic datasets - Work collaboratively with members of the Research, Product, and Engineering teams to develop a research platform for therapeutics target discovery - Regularly present and communicate research to internal and external stakeholders - Represent the 23andMe research team at scientific conferences Required Qualifications: - PhD in Statistics, Statistical Genetics, Computational Biology, Computer Science or a related quantitative field - Strong expertise in statistical genetics and/or computational biology, demonstrated by prior work experience or excellent publication records - Research experience using machine-learning and statistical modeling techniques to analyze large-scale human genetics and genomics datasets - Experience writing robust code in a shared development environment - Proficiency working with very large datasets in R and Python and/or other data analysis languages - Outstanding interpersonal, verbal, and written communication skills - Ability to work in a dynamic, team-oriented, and fast-paced environment Preferred Qualifications: - 1-5 years of postdoctoral/industry experience - Research experience in developing and applying methodology for statistical fine-mapping and variant-to-gene mapping from genetic associations - Bay area location, or willingness to relocate Required Skills: Statistical genetics, Population genetics, Computational biology, Machine learning, Statistical modeling, Genomics datasets analysis, Phenotype modeling, Statistical fine-mapping, Variant-to-gene mapping, R programming, Python programming, Scientific research