Job brief: Lead Bioinformatics & Machine Learning Scientist (Epigenomics)

What JoRoute checked · Checked by JoRoute

• Listed on Natera's own job board. JoRoute does not take listings from job ads or recruiters.
• First seen by JoRoute 2026-11-06 (2 days ago); last confirmed on the board 2026-11-08.
• The apply link opens job-boards.greenhouse.io, the address Natera's board gives for this job.
• No other listing with this title and place on this board in the last 90 days.
• No layoff notices (WARN) under this name in the state files JoRoute reads.

What the listing says · Employer's own words, not checked

• Pay: $172,000 to $214,000 a year
• Job type: Not stated
• Where: San Carlos, CA · US
• Posted: 2026-11-05

What the job asks · Employer's own words, not checked

Level: Lead / staff. The title says "Lead".

Must have

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

Conditions

• Generous paid time off and company holidays

Main duties

• Drive technical workstreams within a collaborative team that takes methods from problem definition through delivery of final classifiers to downstream valida…
• Design, implement, and validate novel machine learning and statistical methods to solve problems in cancer diagnostics
• 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

Lines copied from the posting, not rewritten. "Preferred" or "nice to have" items are not listed as must-haves.

How it compares · Compared by JoRoute from listings

• Across Natera's 220 open listings, 31% of those with a posting date were first posted more than 60 days ago. How Natera hires

What nobody outside the employer knows · Not known

• Whether the role is already filled or on hold: employers often leave listings up.
• How many people applied, and who reads the applications.
• Where in the published range an offer would land.

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