Scientist

Revolution Medicines is a global, commercial-state oncology company dedicated to discovering, developing and delivering innovative medicines for patients with RAS-addicted cancers. Leveraging its differentiated RAS(ON) tri-complex inhibitor platform, the company is advancing a broad, integrated portfolio of oral RAS(ON) inhibitors designed to directly target the active, cancer-driving state of RAS. Founded on rigorous scientific inquiry and a willingness to challenge long-held assumptions, Revolution Medicines is committed to changing the trajectory of disease for patients with RAS-addicted cancers worldwide.

Our people are united by a shared way of working: follow the science, challenge assumptions, act with urgency and hold ourselves to a high standard of rigor—all in service of patients.

The Opportunity:

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.

• Omics & Integrated Biology: Analyze and integrate bulk and single-cell sequencing, spatial transcriptomics, and other molecular data with biomarker, pathology, and efficacy/response outcomes to generate actionable biological hypotheses.

• Scalable Analytics: Develop reproducible analytical workflows and partner on AI-enabled solutions that accelerate exploratory data analysis, visualization, and biological interpretation.

• Cross-functional Collaboration: Partner across Translational Research, Translational Medicine, Discovery, Bioinformatics, and Information Sciences to address key scientific questions across the portfolio.

• Analyze and integrate multimodal molecular and phenotypic datasets from translational studies to identify biomarkers, pharmacodynamic effects, mechanisms of response and resistance, and combination hypotheses.

• Perform cross-study analyses to identify reproducible biological signals and translate computational findings into testable mechanistic and translational hypotheses.

• Partner with experimental scientists on study design and testing of computationally derived biological hypotheses.

• Partner with Biomarker Assays teams to translate insights into actionable biomarker analytes.

• Partner with data science and technology teams to translate scientific needs into scalable analytical and visualization solutions.