Bioinformatics Data Scientist # 4740

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

We are seeking a Data Scientist with strong expertise in cancer genomics and omics data modeling to help us drive the next wave of innovation in early cancer detection. In this role, you will analyze some of the world’s largest and richest genomic and real-world datasets to uncover biological signals, model cancer biology, and identify genomic features that improve test performance. You will apply cutting-edge statistical and machine learning methods to extract biological insights from complex multi-omic datasets and translate them into actionable improvements and new products for clinical oncology applications. The ideal candidate combines deep knowledge of cancer genomics with practical experience in statistical inference and machine learning model development. You will work cross-functionally with computational biologists, assay scientists, machine learning and data engineers, and clinical experts to accelerate innovation, strengthen test performance, and discover cancer biology. 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.

Responsibilities

Analyze and interpret large-scale NGS datasets to identify biological and molecular patterns of cancers related to cancer detection Design, implement and validate innovative statistical methods and machine learning models to extract and interpret cancer genomic signals for product innovation Work closely with interdisciplinary teams (computational, clinical, assay development, and product) to translate data-driven insights to actionable decisions Present and communicate high-quality, evidence-based research findings with clarity and rigor

Required Qualifications

Ph.D. in Cancer Genomics, Statistics, Bioinformatics, Computational Biology, Data Science, Engineering or a related field. Proven track record in working with large-scale omics datasets in R or Python. Proven expertise in genomics — excellent knowledge and hands-on experience on genomics technologies and analysis methods. Familiarity with NGS data processing, statistical modeling, and machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Excellent communication, collaboration, and problem-solving skills; ability to work effectively in interdisciplinary environments.

Preferred Qualifications

Experience in hematological oncology research Knowledge of cancer epigenetics Excellent knowledge of cancer biology, tumor genetics, and molecular mechanisms of oncogenesis Demonstrated ability to integrate biological knowledge with computational modeling to uncover new insights or create new computational tools/methods. Deep understanding of modern machine learning fundamentals and AI techniques for genomics applications, including model development, evaluation, and interpretation. Experience with deep learning and/or large language model (LLM) training or adaptation. Proficiency in Python or R, with experience in modern data science workflows (Linux, version control, reproducible pipelines).