Senior Scientist I, Biomarker Sciences/Translational Sciences

Revolution Medicines•Redwood City, CA

11h•$149,000 - $186,000•Onsite

About The Position

Revolution Medicines is a global oncology company focused on discovering, developing, and delivering medicines for patients with RAS-addicted cancers. The Senior Scientist I, Biomarker Sciences/Translational Sciences will join the Translational Research team and use computational approaches to derive biological and translational insights from complex preclinical and clinical datasets. The role applies computational methods to identify biomarkers, mechanisms of drug response and resistance, pharmacodynamic effects, and therapeutic strategies. It analyzes and integrates bulk and single-cell sequencing, spatial transcriptomics, and other molecular data with biomarker, pathology, and efficacy or response outcomes to generate actionable biological hypotheses. The position also develops reproducible analytical workflows and partners on AI-enabled solutions for exploratory analysis, visualization, and biological interpretation. The Senior Scientist collaborates across Translational Research, Translational Medicine, Discovery, Bioinformatics, Information Sciences, experimental science, Biomarker Assays, data science, and technology teams. The role communicates complex analyses and biological conclusions to multidisciplinary project teams and scientific leadership. The position is full-time and onsite at the company headquarters in Redwood City, California, with pay adjusted for the candidate’s local market. The stated onsite base-pay range is $149,000–$186,000 per year. The company describes its total rewards program as including competitive cash compensation, equity awards, benefits, and learning and development opportunities.

Requirements

PhD in Cancer Biology, Molecular Oncology, Molecular Medicine, Human Genetics, or a related translational biomedical discipline.
Four or more years of relevant postdoctoral and/or industry experience in oncology drug discovery and development.
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 the ability to independently frame biological questions and translate computational findings into testable hypotheses and drug-development insights.
Demonstrated experience and proficiency applying and evaluating machine-learning and emerging AI-enabled approaches to biological data analysis.
Proficiency in R and/or Python for biological data analysis and interpretation, with a 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, including 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 multimodal molecular and phenotypic datasets, including bulk and single-cell sequencing, spatial transcriptomics, biomarker, pathology, and efficacy or response data.
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.
Develop reproducible analytical workflows and partner with data science and technology teams on scalable analytical and visualization solutions.
Evaluate and apply emerging AI/ML approaches to accelerate analysis, data integration, and biological insight generation.
Communicate complex analyses and biological conclusions to multidisciplinary project teams and scientific leadership.