Lead Bioinformatics & Machine Learning Scientist

Natera•San Carlos, CA

21h

About The Position

Natera is seeking a Lead Bioinformatics & Machine Learning Scientist to join its Bioinformatics Research team and build machine learning models for a tissue-free, methylation-based assay. The models will be used to detect cancer in the minimal residual disease (MRD) setting and help inform treatment selection. The role requires a strong background in algorithm development, genomics, sequencing data processing, and applied machine learning. The scientist will develop and evaluate models and algorithms, own research analyses from ideation through implementation and performance evaluation, and develop features for existing pipelines. The role also involves translating between wet-lab experimental design and computational analysis, adapting as assay requirements evolve, and maintaining rigorous quality control across high-volume sequencing data from multiple cohorts, vendors, and clinical protocols. The scientist will communicate findings and model performance to technical and cross-functional stakeholders and contribute to standards for code quality and reproducibility. Natera describes itself as a global leader in cell-free DNA testing, focused on oncology, women’s health, and organ health. The position is eligible for additional compensation and benefits through the company’s Total Rewards program.

Requirements

PhD in Computer Science, Bioinformatics, Computational Biology, or a related quantitative field, with a strong emphasis on cancer epigenomics or genomics.
Four to eight or more 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, with a focus on biological data such as DNA sequencing data.
Strong quantitative reasoning and data analysis skills, applied 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 communicate results orally and in writing to technical and non-technical audiences.
High scientific rigor and eagerness to teach and learn new 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, and drive algorithm design decisions that support cancer detection in the MRD setting.
Own end-to-end research analyses from ideation through implementation, including scripts and notebooks, 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 standards for code quality and reproducibility.