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.

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:

• 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.

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.
• Experience with Python and Linux command-line tools, including writing shell scripts. Strong computational and programming skills, including thorough experience with Python statistical packages (NumPy, Matplotlib, Pandas).
• Familiarity with public databases such as TCGA, COSMIC, or OncoKB.
• Ability to visualize findings and summarize results in oral and written form for both technical and non-technical audiences.
• High scientific rigor, with eagerness to teach and learn new computational methods and biology.
• Ability to work on a cross-functional team in a highly collaborative environment, partnering with both computational and experimental scientists.