23andMe is hiring a quantitative scientist to build predictive models of human health from large-scale genetic, medical, and real-world data. In this hands-on, individual-contributor role, you'll design, develop, and validate risk-prediction models that combine genetic signal with rich EHR and other phenotypic data to predict the incidence, timing, and drivers of health outcomes. We're looking for someone to advance the state of the art of health risk prediction, integrating and extending beyond traditional GWAS and polygenic prediction in a real-world application.With the world's largest database of more than 11 million consented research participants, 23andMe is at the forefront of using human genetics to advance biomedical research and transform healthcare. Join us in helping people access, understand, and benefit from the human genome.What You'll Do- Build predictive models of health outcomes by integrating genomic data with high-dimensional, longitudinal phenotypic data, including electronic health records (EHR).
- Apply time-to-event and survival modeling to predict not just "if" but "when" health events are likely to occur.
- Use a broad toolkit of machine learning and statistical modeling techniques, integrating polygenic scores with non-genetic risk factors, to build risk models that meaningfully improve on what's possible today.
- Validate model performance against external, non-23andMe datasets such as UK Biobank and All of Us.
- Work in close collaboration with product, engineering, and clinical teams to deploy risk models in both direct-to-consumer and clinical settings.
- Communicate your work to both technical and non-technical audiences through discussion, presentations, and scientific conferences, taking ownership of the high-level motivation, interpretation, and application of your projects.
- Publish your work in peer-reviewed journals, demonstrating the utility of integrating genetics into health risk prediction in real-world settings.
What You'll Bring- PhD in statistics, biostatistics, epidemiology, computer science, statistical genetics, or a related quantitative field. Ideal candidates have a background that bridges quantitative modeling and biological expertise.
- Proven ability to act as the primary code author of your analyses and models, writing clear, well-organized, and reproducible code in Python or a similar language, and collaborating in a shared GitHub repository.
- Hands-on experience modeling electronic health record (EHR) or other longitudinal clinical data. Experience working with large biobanks such as UK Biobank or All of Us is a plus.
- A track record of applying machine learning and statistical modeling to large-scale, messy, real-world datasets to predict health outcomes. Strong candidates can demonstrate the translation of this modeling work into specific applications.
- Outstanding interpersonal, verbal, and written communication skills, including the ability to frame your research within the higher-level goals and context of a project.
- Working experience with concepts related to epidemiology and health risk prediction, such as absolute and relative risk, confounding, ascertainment bias, and survival bias.
- Deep expertise in statistical genetics is not required; you should be comfortable treating genetic data (e.g., polygenic scores) as one valuable input to integrate with non-genetic risk factors.
- Understanding of the clinical context in which risk predictions are used.
Strongly Preferred- 1-5 years of postdoctoral or industry experience.
- Bay Area location, or willingness to relocate.
You don't need to meet every qualification to apply. If this work excites you and you meet most of what's here, we'd love to hear from you.Pay Transparency23andMe takes a market-based approach to pay, and amounts will vary depending on your geographic location. The salary range reflected here is for a candidate based in the San Francisco Bay Area. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.
San Francisco Bay Area Base Pay Range
$165,000-$220,000
