Scientist II, Biomarker Sciences/Translation Sciences

Revolution Medicines•Redwood City, CA

4d•$132,000 - $166,000•Onsite

About The Position

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. 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.

Requirements

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.
Strong collaboration and scientific communication skills, with the ability to communicate actionable conclusions to multidisciplinary audiences.

Nice To Haves

Background in oncology drug discovery or development within a biotechnology or pharmaceutical environment.
Experience analyzing and interpreting longitudinal baseline, on-treatment, and resistance/progression datasets.
Ability to integrate sequencing data with proteomics, pathology, ctDNA, pharmacology, and/or clinical outcomes.
Familiarity with cloud-based analytical environments such as Databricks and collaboration on interactive analytical and visualization tools.
Knowledge of RAS/MAPK biology and RAS-driven cancers.

Responsibilities

Apply computational approaches to uncover biomarkers, mechanisms of drug response and resistance, pharmacodynamic effects, and therapeutic strategies across translational studies.
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.
Develop reproducible analytical workflows and partner on AI-enabled solutions that accelerate exploratory data analysis, visualization, and biological interpretation.
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.
Evaluate and apply emerging AI/ML approaches to accelerate analysis, data integration, and biological insight generation.
Clearly communicate complex analyses and biological conclusions to multidisciplinary project teams and scientific leadership.