Senior Data Scientist # 4630

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, please visit grail.com 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. The expected, full-time, annual base pay scale for this position is 156K - $187K.  Actual base pay will consider skills, experience, and location. This role may be eligible for other forms of compensation, including an annual bonus and/or incentives, subject to the terms of the applicable plans and Company discretion. This range reflects a good-faith estimate of the range that the Company reasonably expects to pay for the position upon hire; the actual compensation offered may vary depending on factors such as the candidate’s qualifications. Employees in this role are also eligible for GRAIL’s comprehensive and competitive benefits package, offered in accordance with our applicable plans and policies. This package currently includes flexible time-off or vacation; a 401(k) retirement plan with employer match; medical, dental, and vision coverage; and carefully selected mindfulness programs. GRAIL is an equal employment opportunity employer, and we are committed to building a workplace where every individual can thrive, contribute, and grow. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender, gender identity, sexual orientation, age, disability, status as a protected veteran, , or any other class or characteristic protected by applicable federal, state, and local laws. Additionally, GRAIL will consider for employment qualified applicants with arrest and conviction records in a manner consistent with applicable law and provide reasonable accommodations to qualified individuals with disabilities. Please contact us at [email protected] if you require an accommodation to apply for an open position. GRAIL maintains a drug-free workplace. We welcome job-seekers from all backgrounds to join us! 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 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 experience 2+ 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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