Lead Bioinformatics & Machine Learning Scientist

Natera•San Carlos, CA

9h•$172,000 - $214,000

About The Position

Natera is seeking a Lead Bioinformatics & Machine Learning Scientist for its Bioinformatics Research team. The role will build machine learning models for Natera’s tissue-free, methylation-based assay to detect cancer in the minimal residual disease (MRD) setting and help inform treatment selection. The position calls for expertise in algorithm development, genomics, sequencing data processing, and applied machine learning. The scientist will develop, train, and evaluate models; own research analyses from ideation through implementation and performance evaluation; and develop features for existing pipelines. The role also bridges wet-lab experimental design and computational analysis, adapts to evolving assay requirements, and maintains quality control across high-volume sequencing data from multiple cohorts, vendors, and clinical protocols. The scientist will communicate results to technical and cross-functional stakeholders and help establish standards for code quality and reproducibility.

Requirements

PhD in Computer Science, Bioinformatics, Computational Biology, or a related quantitative field, with a strong emphasis on cancer epigenomics/genomics.
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 developing core machine learning models, including generalized linear models, kernel methods, tree-based algorithms, and neural networks, focused on biological data such as DNA sequencing data.
Strong quantitative reasoning and data analysis skills, with the ability to apply them to relevant scientific problems.
Experience with Python and Linux command-line tools, including writing shell scripts.
Strong computational and programming skills, including thorough experience with Python statistical packages such as NumPy, Matplotlib, and Pandas.
Ability to visualize findings and summarize results orally and in writing for technical and non-technical audiences.
High scientific rigor and eagerness to teach and learn computational methods and biology.
Ability to work collaboratively on a cross-functional team with computational and experimental scientists.

Nice To Haves

Familiarity with public databases such as TCGA, COSMIC, or OncoKB.

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, troubleshooting, and performance evaluation.
Develop new features within existing pipelines.
Translate between wet-lab experimental design and computational analysis as assay requirements evolve.
Maintain rigorous quality control across high-volume sequencing data spanning multiple cohorts, vendors, and clinical protocols.
Communicate findings and model performance to technical and cross-functional stakeholders.
Contribute to establishing standards for code quality and reproducibility.