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Salary: USD 224,000 - 273,000 / annual
insitro is a machine learning-driven drug discovery company that combines causal evidence from human genetics with in vitro cellular data to identify therapeutic targets. This role leads an established team of statistical geneticists responsible for advancing the company's ML statistical genetics platform and extracting novel biological insights.
You will lead, mentor, and grow a team of four statistical geneticists while setting strategic and technical direction for statistical genetics at insitro. Key responsibilities include:
**Team & Technical Leadership**
- Lead, mentor, and develop a team of statistical geneticists
- Define roadmap and priorities for statistical genetics, maintaining high standards for rigor
**Methods & Platform Innovation**
- Advance ML methodology to extract signal from human genetics and integrate with other data modalities
- Partner with software engineering to convert analyses into reusable, well-tested workflows for the research organization
**Target Discovery & Translation**
- Own GWAS, rare variant, and post-GWAS analyses across internal indications to identify and prioritize novel targets
- Move from genetic association to causal hypothesis
- Partner with therapeutic area and drug discovery scientists to convert genetic findings into validated, tractable programs
You will report to the Senior Director, AI/ML (Human Genetics) and work closely with ML scientists, software engineers, computational biologists, and therapeutic area scientists.
The role is hybrid with three days per week in-person at the South San Francisco headquarters, or fully remote within the US.
**Requirements**
- PhD in statistical genetics, human genetics, computational biology, biostatistics, or related field
- 5+ years of experience in statistical genetics
- Deep knowledge of statistical genetics as applied to target discovery in an industry setting
- Direct people management experience with a track record of mentoring and growing technical talent
- Demonstrated experience applying machine learning to genetic, imaging, or other clinical data
- History of publication and methodological innovation in statistical genetics
- Clear communication and cross-functional collaboration skills across experimental scientists and software engineers
**Preferred Qualifications**
- Strong Python or R in a cloud environment, with a track record of reusable, tested, version-controlled code
- Real understanding of a disease area relevant to insitro (cardiovascular, metabolic, liver, ophthalmologic, or neurological)
- Genetic discoveries that have shaped a real drug discovery decision or program
- Experience with high-content modalities such as single-cell or bulk omics, genetic perturbation screening, spatial proteomics, or pooled optical screening
