Dedicated to oncology, women’s health, and organ health. Natera’s cell-free DNA tests help protect health and inform more personalized decisions about care. Backed by Lightspeed and Sequoia.
About the role
Natera is seeking a Lead Bioinformatics & Machine Learning Scientist to join our Bioinformatics Research team and help build the machine learning models behind Natera's tissue-free, methylation-based assay. These will be the models that detect cancer in the minimal residual disease (MRD) setting and help inform treatment selection. The ideal candidate brings a strong background in algorithm development, genomics, sequencing data processing, and applied machine learning. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- PhD in Computer Science, Bioinformatics, Computational Biology, or a related quantitative field with a strong emphasis on cancer epi/genomics and 4 to 8+ years of experience post-PhD
- Deep theoretical and practical understanding of high-throughput DNA sequence data analysis, including mapping, sequence alignment, and variant calling workflows
- Experience in algorithm development and data analysis, including applying and evaluating statistical methods
- Demonstrated experience in developing core ML models, including generalized linear models, kernel methods, tree-based algorithms, and neural networks, with a focus on biological data (e.g., DNA sequencing data)
- Strong quantitative reasoning and data analysis skills, with a demonstrated ability to apply them effectively to relevant scientific problems
- Familiarity with public databases such as TCGA, COSMIC, or OncoKB
More about this role
Natera is seeking a Lead Bioinformatics & Machine Learning Scientist to join our Bioinformatics Research team and help build the machine learning models behind Natera's tissue-free, methylation-based assay. These will be the models that detect cancer in the minimal residual disease (MRD) setting and help inform treatment selection. The ideal candidate brings a strong background in algorithm development, genomics, sequencing data processing, and applied machine learning.
- Develop, train, and evaluate machine learning models, driving algorithm design decisions that support cancer detection in the MRD setting.
- Own end-to-end research analyses, from ideation through implementation (scripts and notebooks), troubleshooting, and performance evaluation, including developing new features within existing pipelines.
- Translate between wet-lab experimental design and computational analysis, navigating ambiguity as assay requirements evolve and maintaining rigorous quality control across high-volume sequencing data spanning multiple cohorts, vendors, and clinical protocols.
- Communicate findings and model performance to both technical and cross-functional stakeholders, and contribute to...
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