Staff Scientist, Machine Learning (Genomics)

Primary Responsibilities

• Lead research on machine learning and deep learning models using NGS data; frame problems, choose approaches, and set technical direction as an individual contributor with Staff-level scope.
• Design, build, and benchmark models and features behind next-generation Women Health and Organ Health products, applying statistical learning and deep learning to high-dimensional, noisy, small-sample sequencing data.
• Work directly with existing sequencing pipelines and outputs, trace problems through processing steps, and build or extend pipeline components when needed.
• Partner with molecular biology and assay development scientists to design experiments, decide what data to generate, and troubleshoot experiments alongside the lab.
• Set the standard for rigorous analysis: sound validation design, honest performance estimates, no leakage between training and evaluation data, and documentation.
• Contribute to Natera's IP portfolio through invention disclosures and patent work, and to scientific reputation through publications and presentations.
• Mentor data scientists and bioinformaticians, review their work, and make decisions with minimal direction.
• Use AI as a routine part of the work, including coding agents for implementation, analysis, and review.

Qualifications

• M.S. or Ph.D. in a quantitative field such as Computer Science, Applied Mathematics, Statistics, Bioinformatics, Computational Biology, Engineering, or similar; equivalent depth built through work counts fully.
• 6+ years of hands-on experience in machine learning and data analysis on biological or genomic data after your degree.
• Demonstrated track record of models or algorithms you led that reached real use (a product, a production pipeline, or a decision someone relied on).
• Depth in next-generation sequencing data analysis: sequencing QC, alignment and quantification, normalization, and associated artifacts.
• Working fluency with NGS bioinformatics pipelines, including running them, reading code and logs, tracing results, and diagnosing failures.
• Extensive practical experience with machine learning, deep learning, and statistics, including model validation and performance estimation on limited or imbalanced data.
• Advanced Python, including open-source ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost) in a cloud environment.
• Experience processing large datasets and working with a query language such as SQL.
• Proven ability to apply mathematical and statistical principles to real estimation problems and turn them into practical solutions.
• Familiarity with assay development and analysis pipelines for molecular or clinical biomarkers.
• Clear communication of technical results to people who do not share your background, and a record of delivering complex projects with cross-functional teams.

Preferred Skills

• Experience with RNA-seq, small RNA/miRNA.
• Experience building probabilistic models for NGS and omics data.
• Experience with diagnostic product development in a regulated or CLIA/CAP setting.
• Experience with workflow orchestration (Nextflow, WDL, Snakemake) and containerization (Docker).
• AWS or GCP for large-scale ML training and inference.
• Patents, peer-reviewed publications, or conference presentations.
• A desire to work in a fast-paced environment where a small team has high impact.