Data Scientist # 4740
TLDR
Analyze large-scale NGS datasets and develop innovative statistical methods to enhance early cancer detection and product innovation in collaboration with interdisciplinary teams.
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Analyze and interpret large-scale NGS datasets to identify biological and molecular patterns of cancers related to cancer detection
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Design, implement and validate innovative statistical methods and machine learning models to extract and interpret cancer genomic signals for product innovation
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Work closely with interdisciplinary teams (computational, clinical, assay development, and product) to translate data-driven insights to actionable decisions
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Present and communicate high-quality, evidence-based research findings with clarity and rigor
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 cancer genomics — excellent knowledge of cancer biology, tumor genetics, and molecular mechanisms of oncogenesis.
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.
Experience in hematological oncology research
Knowledge of cancer epigenetics
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).
GRAIL is a healthcare company focused on advancing early cancer detection through innovative technologies. We leverage next-generation sequencing and large-scale clinical studies, combining expertise from scientists, engineers, and physicians to tackle the challenges of cancer care.
- Founded
- Founded 2016
- Employees
- 201-500 employees
- Industry
- Biotechnology
- Total raised
- $1.7B raised