Senior Bioinformatics Scientist

<p></p> <p>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.</p> <p><strong>Primary Responsibilities:</strong></p> <ul> <li style="line-height: 2;">Develop, train, and evaluate machine learning models, driving algorithm design decisions that support cancer detection in the MRD setting.</li> <li style="line-height: 2;">Own end-to-end research analyses, from ideation through implementation (scripts and notebooks), troubleshooting, and performance evaluation, including developing new features within existing pipelines.</li> <li style="line-height: 2;">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.</li> <li style="line-height: 2;">Communicate findings and model performance to both technical and cross-functional stakeholders, and contribute to establishing standards for code quality and reproducibility.</li> </ul> <p><strong>Qualifications</strong></p> <p></p> <ul> <li style="line-height: 2;">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.</li> <li style="line-height: 2;">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.</li> </ul> <p><strong>Knowledge, Skills, and Abilities:</strong></p> <ul> <li style="line-height: 2;">Deep theoretical and practical understanding of high-throughput DNA sequence data analysis, including mapping, sequence alignment, and variant calling workflows.</li> <li style="line-height: 2;">Experience in algorithm development and data analysis, including applying and evaluating statistical methods.</li> <li style="line-height: 2;">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)</li> <li style="line-height: 2;">Strong quantitative reasoning and data analysis skil