Lead Bioinformatics & Machine Learning Scientist (Epigenomics)

Natera is currently seeking a Lead Bioinformatics & Machine Learning Scientist to contribute to research and development within our Bioinformatics Research and Development team, focused on Epigenomics in Oncology. The team develops machine learning methods for early cancer detection from cell-free DNA methylation data. The ideal candidate will have a strong background in machine learning and statistical modeling, as well as genomics and sequencing data analysis. The successful candidate will be instrumental in developing and expanding state-of-the-art machine learning methods for early detection of cancer, and will provide technical leadership and mentorship within a highly collaborative team.

PRIMARY RESPONSIBILITIES:

Drive technical workstreams within a collaborative team that takes methods from problem definition through delivery of final classifiers to downstream validation teams

Design, implement, and validate novel machine learning and statistical methods to solve problems in cancer diagnostics

Establish and promote best practices in model interpretability, uncertainty estimation, and reproducibility

Design robust feature engineering and extraction pipelines tailored to cfDNA methylation and sequencing data

Collaborate closely with molecular biologists on experimental design, ensuring data integrity and quality control

Mentor other scientists and foster a culture of innovation, collaboration, and scientific excellence

Lead and actively participate in code and design reviews

QUALIFICATIONS:

PhD in Bioinformatics, Computer Science, Machine Learning, Statistics, or a related quantitative field, with 4 to 8+ years of post-PhD experience

Hands-on experience in cancer genomics or epigenomics; experience with cell-free DNA or methylation sequencing data is a plus

Strong foundation in quantitative reasoning, statistical analysis, and applied machine learning; experience with deep learning frameworks is a plus

Proven track record of developing computational methods for high-throughput sequencing data analysis

Strong programming skills, with hands-on experience in Python and its scientific library stack (pandas, NumPy, scikit-learn)

Experience with Linux command-line tools, shell scripting, version control (Git), workflow managers (e.g., Nextflow, Snakemake), and cloud or HPC compute environments

Ability to perform hypothesis-driven research, visualize findings, and summarize results in oral and written form

Experience mentoring or technically leading other scientists

The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.

Remote USA

$146,000—$180,000 USD