Senior Bioinformatics Scientist

About this role

Natera is seeking a Senior Bioinformatics 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.

Primary Responsibilities: 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 establishing standards for code quality and reproducibility. Qualifications Ph.D. in Bioinformatics, Computer Science, Engineering, Biochemistry, or a related field, with a strong focus on cancer epi/genomics with 0-3 years of professional experience. Master in Bioinformatics, Computer Science, Engineering, Biochemistry, or a related field, with a strong focus on cancer epi/genomics with 4-6 years of professional experience.

Knowledge, Skills, and Abilities: 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.