Senior Scientist, Clinical Biostatistics & Genetic Risk Prediction

Position Overview:

We're seeking a senior scientist with a strong background in clinical statistics and human genetics to design and analyze the clinical studies that establish the utility of our risk prediction models in real clinical settings. You'll own trial design, estimands, and Statistical Analysis Plans, and you'll work directly with the data using reproducible code. This is a key role on a small, self-sufficient, and versatile team: the successful candidate will roll up their sleeves to deliver the analysis end-to-end, transforming ambiguous questions into defensible, reproducible, and impactful findings.

What You'll Do:

• Design prospective, biomarker-stratified studies to demonstrate the clinical utility of our models; define estimands, endpoints, and analysis strategies.
• Develop and execute Statistical Analysis Plans, sample-size and power calculations, and primary, sensitivity, and subgroup analyses consistent with ICH E9 / E9(R1).
• Pull, clean, and analyze complex data (e.g. biobank cohorts (UK Biobank, All of Us), EHR extracts, registries) in reproducible and well-structured code.
• Apply statistical genetics methodology (calibration, ancestry portability, confounding, population stratification) to study design and score evaluation.
• Apply survival, longitudinal, and causal/observational methods to real-world evidence that complements trial data.
• Partner with medical, regulatory, and clinical operations teams and support trial-related regulatory interactions.

What You'll Need:

• Ph.D. in Biostatistics, Statistical Genetics, Genetic Epidemiology, or a related quantitative field and 4+ years of professional experience in a similar role.
• Demonstrated clinical study / trial design experience: SAP authorship, sample-size/power, estimands, and working command of ICH E9.
• Statistical genetics or genetic epidemiology experience.
• Demonstrated ability to take raw, poorly-structured data and produce clean, reproducible analyses independently. 
• Proficiency in Python and/or R, version control (Git), and large genomic/clinical datasets in cloud environments (AWS / GCP / DNAnexus).
• Survival and longitudinal modeling; causal inference for observational data.
• Strong written and verbal communication skills to communicate complex statistical concepts to non-technical audiences.

Preferred / Bonus Qualifications:

• Experience in a start-up environment.
• Population-scale biobank experience (UK Biobank, All of Us, MVP).
• Strong publication record that demonstrates initiative and scientific independence.
• Direct work on a genetics clinical-utility or decision-impact study.
• Regulatory trial experience (IDE, FDA interactions) or clinical validation for diagnostics / SaMD

Location, Compensation, and Benefits:

• Location: Hybrid, 2–3 days onsite at our Menlo Park, CA office; Bay Area candidates preferred. Remote in the US considered for the right candidate.
• Compensation: Annual salary range of $160,000 - $180,000, commensurate with experience. This role is also eligible for equity.