Lead Bioinformatics & ML Scientist — Cancer Genomics

Natera; San Carlos (CA); On-site; USD 172,000 - 214,000; Full time

Benefits offered by this job

Annual bonus

Equity awards

401(k) with match

Paid time off

Job summary

Natera is seeking a Lead Bioinformatics & Machine Learning Scientist to advance machine learning models behind our tissue-free, methylation-based cancer assay. You will lead end-to-end analyses, translate experimental design to computational workflows, and drive rigor in sequencing data processing across multiple cohorts.

The role requires a PhD with 4–8+ years post-PhD, deep sequencing analytics experience, and strong Python/Linux skills.

Qualifications

• PhD in CS/Bioinformatics/Computational Biology or related quantitative field with 4–8+ years post-PhD.
• Strong background in high-throughput DNA sequence data analysis and cancer epi/genomics.
• Experience in developing core ML models (GLMs, kernels, trees, neural nets) for biological data.
• Proficiency in Python and Linux, including shell scripting and data analysis libraries (NumPy, Pandas, Matplotlib).
• Familiarity with public databases such as TCGA, COSMIC, or OncoKB.

Responsibilities

• Develop, train, and evaluate ML models for cancer detection in MRD settings.
• Own end-to-end research analyses from ideation to implementation and evaluation.
• Bridge wet-lab design and computational analysis, ensuring QC across high-volume sequencing data.
• Communicate findings to technical and non-technical stakeholders and contribute to code quality standards.

Skills

High-throughput sequencing analysis

ML model development

Python

Linux

Shell scripting

NumPy/Pandas/Matplotlib

Data visualization

Education

PhD in Computer Science, Bioinformatics, Computational Biology or related field

Tools

TCGA

COSMIC

OncoKB

Job description

Natera is seeking a Lead Bioinformatics & Machine Learning Scientist to advance machine learning models behind our tissue-free, methylation-based cancer assay. You will lead end-to-end analyses, translate experimental design to computational workflows, and drive rigor in sequencing data processing across multiple cohorts.

The role requires a PhD with 4–8+ years post-PhD, deep sequencing analytics experience, and strong Python/Linux skills.