Computational Clinical Scientist II, Clinical Bioinformatics

Revolution Medicines•Redwood City, CA

3d•Onsite

About The Position

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company’s RAS(ON) inhibitors daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, are currently in clinical development. As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway. In this role, you will work within our Translational Medicine group analyzing and interpreting molecular and clinical data. Analysis will focus on using genomic, protein and other targeted assays to identify and validate predictive biomarkers. The computational clinical scientist will work on various projects in different phases of drug development and contribute to the cross-functional team which includes other bioinformaticians, program leads, medical monitors, biostatisticians other pre-clinical scientists.

Requirements

M.S. or Ph.D. in bioinformatics, biology, data science or a related field with emphasis on biological data analysis.
2+ years of pharmaceutical or biotech experience in bioinformatics focused on oncology, immunology, or related field.
Proficiency and experience programming using R and/or Python.
Proficiency in statistical models and analysis of clinical data.
Proficiency in visualizations and faceting of molecular data necessary (oncoplots, heatmaps, PCA, etc.).
Experience with efficacy, baseline demographic, and other outcome data generated in a clinical trial.
Experience with statistical methods i.e. survival analysis, and causal inference.
Expertise in bioinformatic analysis of cancer signaling pathways and driver mutations.

Nice To Haves

Some ML experience a strong plus.
Experience in tools to analyze cancer genomics data types (RNA, WES, ctDNA, etc) and common tool outputs (GATK, Salmon, OncoKB, VEP, etc.) preferred.
Development with professional software development systems and methods (e.g. IDE’s, ticketing systems, version control systems).
Familiarity with large public/private genomic data sets (GDC, TCGA, etc.).
Familiarity with clusters, containers, and cloud computing infrastructure for data processing, analysis, and storage.
Experience in tools to analyze cancer genomics datasets (GATK, Salmon, OncoKB, VEP, etc.).

Responsibilities

Integrate multiple data sources including circulating tumor DNA, patient enrollment data, molecular profiling (RNA/DNA sequencing outputs), multiplex immunohistochemistry to identify and validate new biomarkers for candidate drugs.
Work with internal groups, contract research organizations and collaborators to deliver validated biomarker data and analysis.
Support the development of biomarker related aspects of the trial design where bioinformatic analysis is required, including study protocols, lab manuals, clinical study reports, and other regulatory documents.
Work with biomarker scientists to support analysis of biomarker readouts (summary statistics, and visualization of efficacy in molecular subgroups).
Collaborate with preclinical bioinformaticians to design, analyze and interpret experiments to validate biomarkers, understand compound mechanism of action, and identify biomarkers for patient selection and target engagement.