# Scientist II, Biomarker Sciences/Translation Sciences at Revolution Medicines

- Company: Revolution Medicines
- What the company does: Backed by GV.
- Type: Startups
- Level: Mid level
- Location: Redwood City, California, United States
- Work setup: On-site
- Posted: 2026-09-17
- Apply by: 2026-11-01
- Apply: https://www.revmed.com/careers-list/?gh_jid=7997809003
- Page: https://www.1752.vc/careers/jobs/revolution-medicines-scientist-ii-biomarker-sciences-translation-sciences/

## About the role

As a Scientist II, Biomarker Sciences/Translational Sciences, you will be a key member of the Translational Research team, applying computational approaches to derive biological and translational insights from complex preclinical and clinical datasets. You will: Translational Science: Apply computational approaches to uncover biomarkers, mechanisms of drug response and resistance, pharmacodynamic effects, and therapeutic strategies across translational studies.

## What they're looking for

- PhD in Cancer Biology, Molecular Oncology, Molecular Medicine, Human Genetics, or a related translational biomedical discipline, with 3+ years of relevant postdoctoral and/or industry experience
- Substantial hands-on experience analyzing and biologically interpreting complex omics datasets, including bulk and single-cell sequencing, spatial transcriptomics, and integrated molecular, phenotypic, and clinical/translational data
- Strong background in oncology, cancer genomics, and translational biology, with demonstrated ability to independently frame biological questions and translate computational findings into testable hypotheses and drug-development insights
- Demonstrated experience and proficiency in applying and evaluating machine learning and emerging AI-enabled approaches to biological data analysis
- Proficiency in R and/or Python for the analysis and interpretation of biological data, with a strong commitment to reproducible and rigorous analytical practices
- Strong foundation in statistical analysis and experimental design for high-dimensional biological and biomarker data

Tags: Translational Research