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# Staff, Bioinformatics Data Scientist

- **Company:** Rezo Therapeutics, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $180,000.0 - $260,000.0
- **Contract:** Permanent contract
- **Skills:** Bioinformatics, Code Review, Computational Biology, Python (Programming Language), Raw Data
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/staff-senior-staff-bioinformatics-data-scientist-rezo-therapeutics-7998717

## About the Role

* PhD in computational biology, bioinformatics, systems biology, cancer biology or related field with 8+ years of industry experience.
 * Deep experience analyzing at least three of the following -omic datasets: transcriptomics, proteomics and their variants (e.g. phosphoproteomics, etc.), AP-MS / interactomic data and functional genomics (e.g. DepMap, CRISPR screens).
 * Strong python engineering skills. Comfortable working in a shared codebase and using software engineering best practices, including code reviews.
 * Clear demonstrations of the ability to take a biological question from raw data to a concrete mechanistic or translational conclusion.
 * Working fluency in cancer biology, including oncogenic signaling, tumor suppressors, cell cycle and the use of biomarker classification to distinguish sensitive versus resistant populations.
 * Proven ability to work independently while fostering collaboration in a team environment.
 * Excellent organizational, communication, and interpersonal skills. Compensation at Rezo

## Description

* Design and implement analyses across Rezo's protein network datasets, refining existing approaches and developing new methods as needed.
 * Integrate multi-omics data to build mechanistic and translational understanding of our programs, spanning from mechanism of action to patient biomarker selection strategies.
 * Build internal tools and interfaces that enable the rest of the company to explore the data and make decisions based on conducted analyses.
 * Familiar with ML methods, including interpreting results and experience curating data for ML methods.
 * Communicate results clearly in writing, figures and presentations so that the analyses can be translated into program decisions.


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