Lead Bioinformatics & ML Scientist - Cancer Genomics

Remote Genie; United States; Remote; USD 146,000 - 182,000; Full time

Benefits offered by this job

Competitive Benefits

Free genetic testing for employee and

401k benefits

Commuter benefits

Employee referral program

Job summary

Natera is seeking a Lead Bioinformatics & Machine Learning Scientist to join our Bioinformatics Research team to build models for the tissue-free, methylation-based assay. You will develop ML models for cancer detection in MRD, oversee end-to-end analyses, and bridge wet-lab and computational workflows across multiple cohorts and clinical protocols.

The ideal candidate holds a PhD with 4–8+ years post-PhD, expertise in genomics, Python, Linux, and statistical methods, and can communicate

Qualifications

• PhD with 4 to 8+ years of experience post-PhD in relevant quantitative field.
• Strong background in high-throughput DNA sequence data analysis and variant calling workflows.
• Experience developing core ML models for biological data (DNA sequencing).
• Ability to communicate results to both technical and non-technical audiences.

Responsibilities

• Develop, train, and evaluate ML models for cancer detection in MRD setting.
• Own end-to-end research analyses from ideation to implementation and evaluation.
• Translate wet-lab design to computational analysis and maintain QC across high-volume sequencing data.
• Communicate findings and establish standards for code quality and reproducibility.

Skills

DNA sequencing data analysis

Python

Linux

Data analysis

Machine learning models

Statistical methods

Public databases (TCGA COSMIC OncoKB)

Education

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

Tools

NumPy

Pandas

Matplotlib

Job description

Natera is seeking a Lead Bioinformatics & Machine Learning Scientist to join our Bioinformatics Research team to build models for the tissue-free, methylation-based assay. You will develop ML models for cancer detection in MRD, oversee end-to-end analyses, and bridge wet-lab and computational workflows across multiple cohorts and clinical protocols.

The ideal candidate holds a PhD with 4–8+ years post-PhD, expertise in genomics, Python, Linux, and statistical methods, and can communicate