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Senior Computational Clinical Scientist
| Company | Revolution Medicines |
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| Location | San Carlos, CA, USA |
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| Salary | $158000 – $198000 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Senior |
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Requirements
- M.S. or Ph.D. in bioinformatics, biology, data science or a related field with emphasis on biological data analysis.
- 3+ 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. Some ML experience a strong plus.
- 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.
- Experience in tools to analyze cancer genomics data types (RNA, WES, ctDNA, etc) and common tool outputs (GATK, Salmon, OncoKB, VEP, etc.) preferred.
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.
Preferred Qualifications
- 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.).
