insitro's mission is to bring better drugs faster to the patients who can benefit most, through machine learning and data at scale. Our strategy combines causal evidence from human genetics with in vitro cellular data from our automated wet-lab platforms to find causal genetic intervention points and carry them into therapeutic programs.
This position reports to the Senior Director, AI/ML (Human Genetics), and is remote-eligible within the US or based in South San Francisco with an in-person hybrid schedule of three days per week.
Team & Technical Leadership
Lead the Team: Lead, mentor, and grow a team of four statistical geneticists developing methods to extract insight from human genetics
Set the Direction: Define the roadmap and priorities for statistical genetics at insitro, and hold the bar for rigor
Methods & Platform Innovation
Advance the Methods: Lead the team in building new ML methodology that extracts more signal from human genetics and integrates it with other data modalities
Make It Reusable: Partner with software engineering to turn one-off analyses into well-tested workflows the whole research organization can run
Target Discovery & Translation
Own the Analysis: Own GWAS, rare variant, and post-GWAS analyses across internal indications to identify and prioritize novel targets, moving from association to causal hypothesis
Translate to Programs: Partner with therapeutic area and drug discovery scientists to convert genetic findings into validated, tractable programs
Proven Tenure: PhD in statistical genetics, human genetics, computational biology, biostatistics, or a related field, plus 5+ years of experience
Statistical Genetics Depth: Deep knowledge of statistical genetics as applied to target discovery in an industry setting
People Leadership: Direct people management experience, with a track record of mentoring and growing technical talent
ML Fluency: Demonstrated experience applying machine learning to genetic, imaging, or other clinical data
Published Innovator: A history of publication and methodological innovation in statistical genetics
Core Competencies
Cross-functional Communicator: You communicate clearly and collaborate easily across functions, including with experimental scientists and software engineers
Preferred
Engineering Fluency: Strong Python or R in a cloud environment, with a track record of reusable, tested, version-controlled code
Disease Area Depth: Real understanding of a disease area relevant to insitro — cardiovascular, metabolic, liver, ophthalmologic, or neurological
Discovery Track Record: Genetic discoveries that have shaped a real drug discovery decision or program
Multi-modal Experience: Work with high-content modalities such as single-cell or bulk omics, genetic perturbation screening, spatial proteomics, or pooled optical screening
