Senior Scientist I, Biomarker Sciences/Translational Sciences

Revolution Medicines•Redwood City, CA

10h•$149,000 - $186,000

About The Position

Revolution Medicines is seeking a Senior Scientist I for Biomarker Sciences/Translational Sciences to join its Translational Research team. The role applies computational approaches to complex preclinical and clinical datasets to uncover biomarkers, drug-response and resistance mechanisms, pharmacodynamic effects, and therapeutic strategies across translational studies. The scientist will analyze and integrate 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 role also develops reproducible analytical workflows and partners on AI-enabled approaches for exploratory analysis, visualization, and biological interpretation. It involves close collaboration across Translational Research, Translational Medicine, Discovery, Bioinformatics, Information Sciences, Biomarker Assays, and data science and technology teams, and communicating conclusions to multidisciplinary teams and scientific leadership.

Requirements

PhD in Cancer Biology, Molecular Oncology, Molecular Medicine, Human Genetics, or a related translational biomedical discipline.
4+ 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 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

Analyze and integrate multimodal molecular and phenotypic data 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 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.
Communicate complex analyses and biological conclusions to multidisciplinary project teams and scientific leadership.