Senior Data Scientist

About GRAIL

Our mission is to detect cancer early, when it can be cured. We are working to change the trajectory of cancer mortality and bring stakeholders together to adopt innovative, safe, and effective technologies that can transform cancer care.

We are a healthcare company, pioneering new technologies to advance early cancer detection. We have built a multi-disciplinary organization of scientists, engineers, and physicians and we are using the power of next-generation sequencing (NGS), population-scale clinical studies, and state-of-the-art computer science and data science to overcome one of medicine’s greatest challenges.

GRAIL is headquartered in the Bay Area of California, with locations in Washington, D.C., North Carolina, and the United Kingdom. It is supported by leading global investors and pharmaceutical, technology, and healthcare companies.

For more information, visit

GRAIL is seeking a Senior Data Scientist to join the Computational Biology team. In this role you will work with population-scale genomic datasets from our on-market commercial product and first-in-class clinical studies to model real-world interactions between technical, biological and clinical factors that underlie current test performance and gain insights into future innovations.

You will be developing novel, state-of-the-art methods and tools that will be used to monitor the output of our in-production multi-cancer classifier. Your work will be critical to the success of a new generation of cancer detection products by defining the parameters that ensure a high level of classifier performance over time. The team works cross-functionally across Grail to connect the knowledge behind our advanced cancer detection technology with the clinical variables that define real-world usage as the product scales to large-scale adoption.

The position is an exciting opportunity at the intersection of machine learning and clinical genomics to support GRAIL’s mission to detect cancer early, when it can be cured.

This role is based in Menlo Park, California, and will move to Sunnyvale, California, in Fall 2026. It offers a flexible work arrangement, with the ability to work from GRAIL’s office or from home. 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.

At our Menlo Park campus, Tuesdays and Thursdays are the key days where we encourage on-site presence to engage in events and on-site activities.

• Analyze complex high‑dimensional datasets related to multi-cancer early detection test results from Grail’s commercial platform in order to identify empirical trends and then communicate findings across teams.
• Integrate cancer biology, DNA methylation, genomics, epidemiology, and statistics to generate predictive models of expected test performance and identify potential deviations.
• Participate in cross‑functional interactions with interdisciplinary teams including machine learning, software engineering, clinical, laboratory operations, research, and product development.
• Create and communicate rigorous scientific analyses.

• Ph.D. in Bioinformatics, Data Science, Computational Biology, Physics, Bioengineering, Cancer Genomics, Statistics, Biochemistry or a related field with 2+ years of relevant experience
• Proven track record in working with large‑scale omics datasets in R (preferred) or Python.
• Experience with NGS data processing, statistical modeling, and machine learning frameworks and their application to derive technical and biological insights in a clinical setting
• Excellent communication, collaboration, and problem‑solving skills; ability to work independently and effectively across interdisciplinary environments.
• Demonstrated ability to perform rigorous and detail‑oriented work.

• Knowledge of cancer epigenetics, cancer biology, tumor genetics, and molecular mechanisms of oncogene sis
• Experience with traditional ML and modern…