GRAIL is seeking a Senior Data Scientist to join the Machine Learning team within the Computational Biology and Machine Learning (CBML) group. In this role, you will work at the intersection of machine learning, genomics, and clinical science to advance early cancer detection. You will collaborate closely with scientists, engineers, and clinicians to identify novel biological signals, improve classification performance, and develop innovative approaches for cancer detection and categorization using GRAIL’s rich sequencing datasets.
This is a highly impactful role where you will apply state-of-the-art machine learning techniques—including modern AI approaches—to real-world clinical challenges. Your work will directly contribute to scientific discoveries, peer-reviewed publications, and the development of transformative products for early cancer detection.
This is a hybrid role based in Menlo Park, CA (moving to Sunnyvale, CA in Fall 2026). Our current flexible work arrangement policy requires that a minimum of 40%, or 16 hours, of your total work week be on-site. Your specific schedule, determined in collaboration with your manager, will align with team and business needs and could exceed the 40% requirement for the site.
Responsibilities:
Envision, design, and lead projects to evaluate and improve machine learning classifier performance for cancer detection
Collaborate cross-functionally with scientists, engineers, and clinicians to plan, execute, and interpret experiments
Develop high-quality, reproducible, and scalable software aligned with sound engineering principles
Apply best practices in machine learning and statistics to generate robust, interpretable, and reliable results
Analyze large-scale sequencing and genomics datasets to extract meaningful biological insights
Contribute to the development and evaluation of novel machine learning methods, including deep learning approaches
Communicate findings and present updates regularly in technical and cross-functional forums
Contribute to scientific publications, internal tools, and production systems
These responsibilities summarize the role’s primary responsibilities and are not an exhaustive list. They may change at the company’s discretion.
Required Qualifications
Ph.D. in Bioinformatics, Computational Biology, Computer Science, Statistics, Machine Learning, or a related field with 2+ years of relevant experience, OR
M.S. with 4+ years of relevant experience, OR
B.S. with 6+ years of relevant experience, or equivalent practical experience2+ years of experience applying machine learning or statistical modeling in a research or production environment
Strong expertise in data analysis using Python or R
Deep understanding of modern machine learning and statistical methods
Experience developing reproducible, well-structured code in a collaborative environment
Strong written and verbal communication skills
Preferred Qualifications:
Experience with modern AI techniques, including deep learning and/or large language model (LLM) training or adaptation
Experience working with sequencing or genomics data and deriving biological insights
Track record of scientific contributions (e.g., publications, tools, datasets, patents, or conference presentations)
Experience with system-level programming languages (e.g., Go, Java, C, C++)
Familiarity with version control (e.g., Git) and reproducible research practices in Linux environments
Demonstrated ability to independently drive projects while collaborating effectively across teams
Interest in translating research innovations into production-ready systems
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