About Us
DELFI Diagnostics, Inc. (DELFI Diagnostics) is developing next-generation, blood-based tests that are reliable, accessible and deliver a new way to help detect cancer. Employing advanced machine-learning methods to whole-genome sequencing data, the DELFI ("DNA EvaLuation of Fragments for early Interception") platform is built to address the highest-burden health challenges. DELFI Diagnostics prioritizes solutions that have the potential to save lives on a global scale, including for historically underserved populations. DELFI Diagnostics’ platform relies on fragmentomics – the discovery that cancer cells are more chaotic than normal cells and, when they die, leave behind tell-tale patterns and characteristics of cell-free DNA (cfDNA) fragments. FirstLook Lung, for individuals eligible for lung cancer screening, is DELFI Diagnostics’ first laboratory-developed screening test and can be part of routine blood work. FirstLook Lung uses millions of data points to reliably identify individuals who may have cancer detected through low-dose CT, including early stage disease with a negative predictive value of 99.8 percent. This test has not been cleared or approved by the FDA.
In our passionate pursuit to radically improve health outcomes, we serve humanity when we:
Lead with Science, Anchor in Pragmatism: We pioneer life-changing science by ensuring quality, transparency, and rigor at all times. We explore thoughtfully, experiment smartly, and deliver impact with conviction.
Build With & For All: We embrace diverse backgrounds to innovate and achieve together. We are not just building a product - we aim to disrupt the path of cancer for all - no matter geography or socioeconomic class
Put We over I: We are a home for high-performing people. Through teamwork, we build collective intelligence. Each of us wins when those we serve and those who serve with us--win. We show up with empathy, humility, and integrity at every step of the journey.
DELFI has 1-2 designated in-office working days each week for employees who live within within 50 miles of Palo Alto, CA
About the Role
We're hiring an experienced data scientist to analyze and model high-dimensional molecular data across emerging data modalities in support of our oncology-focused cancer detection platform. You'll work at the intersection of cancer biology and data science, owning projects end-to-end while partnering closely with assay development and experimental teams. This is a high-impact role for someone who thrives in ambiguity and is energized by hard problems in early cancer detection.
What You'll Do
Own end-to-end analysis of high-dimensional molecular workstreams across multiple data modalities, from data processing through interpretation and communication of results.
Contribute to classifier design, development, and validation to detect cancer in prospective clinical studies
Adapt external, state-of-the-art representation and foundation models for DELFI’s internal applications
Collaborate closely with Assay, Software, and Clinical teams to evaluate and integrate emerging data types into analytical pipelines
Support data science coverage across active studies, including assay research, characterization and validation
Work effectively in a fast-moving environment where priorities shift and self-direction is expected
What You'll Bring
Required
Doctoral degree in Computational Biology, Bioinformatics, Data Science, or a related field with 2+ years of industry experience; or, Master's with 3+ years of industry experience; or, Bachelor’s with 5+ years of industry experience
Hands-on experience with high-dimensional biological datasets across multiple biomarkers and molecular data types
Background in cancer biology or oncology-focused research
Demonstrated ability to own projects end-to-end with limited oversight
Demonstrated ability to use experimental findings to drive decisions for future research or product directions
Strong collaborator with wet lab and experimental teams
Proficiency in Python; experience with cloud environments (AWS preferred)
Preferred
Experience with cfDNA, liquid biopsy, and NGS-based platforms
Familiarity with ML modeling in a biological context
Experience in a regulated diagnostics or clinical setting
