Principal Scientist, Bioinformatics Human Genetics
Genentech · South San Francisco, CA · Research & Development
About this role
Genentech is hiring a principal-level Data Scientist based in South San Francisco, CA. The posting calls out experience with Python, R, Git, Machine Learning. Compensation is listed at $150,700–$279,900 per year.
- Role
- Data Scientist
- Function
- data engineering
- Level
- principal
- Track
- Tech leadership
- Employment
- Full-time
- Location
- South San Francisco, CA
- Department
- Research & Development
- Posted
- Mar 22, 2026
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Job description
from Genentech careersThe Position
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Genentech.
Genentech’s Department of Human Genetics sits at the center of our precision medicine strategy. We combine large-scale human genetic evidence with rich molecular, cellular, and clinical data to uncover causal disease biology and translate it into actionable targets, biomarkers, and patient stratification strategies.
THE OPPORTUNITY
We are seeking a Principal Scientist (Bioinformatics track) to lead cutting-edge statistical genetics and AI/ML-enabled “sequence-to-phenotype” research that directly accelerates target discovery and translation—particularly in Neuroscience (e.g., Multiple Sclerosis, Parkinson’s disease, ALS). This is a methods-forward role for a scientist who thrives at the interface of human genetics, multimodal genomics, and machine learning, and who wants to see their work influence real therapeutic decisions.
In this role, you will:
Lead end-to-end human genetics analyses across array and sequencing cohorts, including rigorous QC, GWAS, fine-mapping, colocalization, gene prioritization, and rare-variant association (e.g., gene-based burden and variance-component approaches), with attention to multi-ancestry and bias/robustness.
Integrate genetics with functional and multimodal genomics, including single-cell and multiome (RNA/ATAC), molecular QTLs, and perturbation datasets to identify causal genes, implicated cell types/states, and mechanistic hypotheses.
This is an excerpt. Read the full job description on Genentech careers →