
Staff Machine Learning and Bioinformatics Scientist (Multi-Cancer Early Detection)
About this role
The Staff Machine Learning and Bioinformatics Scientist (Multi-Cancer Early Detection) position at Natera is a full-time remote role based in the US that suits experienced quantitative scientists specializing in multi-modal genomic data and cancer signal detection. This professional will design predictive models to identify various cancers from a single blood draw and guide technical projects from initial prototype to clinical validation.
Role Overview
The research team at Natera builds advanced tests to detect multiple cancers through blood samples. This position focuses on creating the statistical and machine learning models that form the core of that diagnostic effort. The selected candidate collaborates directly with clinical experts and molecular biologists to advance methodologies for patient care.
Key Responsibilities
Requirements
Nice to Have
Skills and Tools
Salary and Benefits
About the Company
Natera functions as a global provider of cell-free DNA testing, focusing on organ health, oncology, and women's health. The organization aims to integrate personalized genetic testing into standard medical care to facilitate earlier and more targeted health interventions.
How to Apply
Interested candidates should apply directly on the official Natera careers page.
Skills and tech stack
Questions candidates ask
What is the salary range for this position?
The salary ranges from $163,200 to $204,000 USD, depending on factors such as candidate experience, skill set, certifications, and location.
Where is this job located?
This is a US Remote position.
What are the educational requirements for applicants?
Applicants must hold a PhD in Bioinformatics, Computer Science, Mathematics, or a related quantitative field.
How many years of experience are required post-PhD?
Candidates need 6 to 12 years of experience post-PhD.
What programming language is required?
Proficiency in Python and its scientific computing ecosystem is required.