GRAIL is a pioneering healthcare company dedicated to detecting cancer early, when it can be cured. By combining next-generation sequencing (NGS), population-scale clinical studies, and state-of-the-art computer science, GRAIL is working to transform cancer care and reduce global cancer mortality.
The Opportunity
GRAIL is seeking a Data Scientist with deep expertise in cancer genomics and omics data modeling. You will analyze some of the world’s largest genomic and real-world datasets to identify biological signals and genomic features that improve test performance.
This role requires a blend of cancer biology knowledge and practical experience in statistical inference and machine learning. You will work cross-functionally with computational biologists, assay scientists, and clinical experts to extract actionable insights from complex multi-omic data.
What You'll Do
- Data Analysis: Analyze and interpret large-scale NGS datasets to identify molecular patterns related to cancer detection.
- Model Development: Design, implement, and validate innovative statistical methods and machine learning models for product innovation.
- Cross-functional Collaboration: Partner with clinical, assay development, and product teams to translate data insights into clinical oncology applications.
- Communication: Present high-quality, evidence-based research findings with clarity and scientific rigor.
What You'll Bring
Required Qualifications
- Education: Ph.D. in Cancer Genomics, Statistics, Bioinformatics, Computational Biology, Data Science, or a related field.
- Programming: Proven track record of working with large-scale omics datasets in R or Python.
- Technical Expertise: Excellent knowledge of genomics technologies and analysis methods, including NGS data processing.
- Frameworks: Familiarity with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
Preferred Qualifications
- Domain Knowledge: Experience in hematological oncology research and knowledge of cancer epigenetics.
- Biology Expertise: Deep understanding of tumor genetics and molecular mechanisms of oncogenesis.
- Advanced AI: Experience with deep learning and/or Large Language Model (LLM) training and adaptation.
- Workflow Proficiency: Experience in modern data science workflows (Linux, version control, reproducible pipelines).
Location & Work Arrangement
- Current Site: Menlo Park, California.
- Future Site: Moving to Sunnyvale, California in Fall 2026.
- Arrangement: Flexible/Hybrid. A minimum of 40% (16 hours) per week is required on-site, with Tuesdays and Thursdays being the primary anchor days.
