Revolution Medicines is a clinical-stage precision oncology company focused on developing novel targeted therapies to inhibit frontier targets in RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) Inhibitors designed to suppress diverse oncogenic variants of RAS proteins, and RAS Companion Inhibitors for use in combination treatment strategies. As a new member of the Revolution Medicines team, you will join other outstanding scientists in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway.
The Opportunity:
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Be the founding computational member of the new Genetic Pharmacology team within Translational Research (part of the larger Biology group), focused on understanding the relationship between tumor genetics, microenvironment, and response to targeted therapies using gold-standard genetically engineered mouse models (GEMMs) of cancer. The position will report to the Director of Genetic Pharmacology.
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Develop analytical methods to understand the effect of genetic alterations on tumor growth (prognostic biomarkers) and response to therapy (predictive biomarkers) in vivo.
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Analyze diverse data spanning functional genomics (NGS), WES, RNA-seq, in vivo imaging-based readouts, histology, spatial and single cell analyses, etc.
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Work in close iteration with our cancer/molecular biologists to design experiments and interpret data to most effectively extract statistically and biologically meaningful insights from our team’s work.
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Contribute to the cross-functionality of Genetic Pharmacology by collaborating closely with the central Bioinformatics team to build robust, rigorous, and accurate analytics pipelines.
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Conduct analyses and meta-analysis of internal and external datasets to inform our understanding of therapy combination strategies and patient selection strategies (genetic biomarkers across different cancer indications).
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Monitor data quality and accuracy, identify sources of noise in the data, and drive improvements to data richness and resolution, including improvements to both wet and dry lab processes.
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Contribute to the organization, planning, prioritization, and management of projects running in parallel within an innovative, high-energy, and fast-paced team.
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Present results at internal meetings and scientific conferences.
Required Experience, Skills, and Education:
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A Ph.D. in bioinformatics, computational biology, statistics, applied math, physics or a related field with 8+ years of postdoctoral/industry experience leveraging quantitative methods to understand biology.
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Deep expertise in biological data analysis and visualization.
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Strong foundation in probability and statistics.
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Experience manipulating and analyzing large biological datasets in Python.
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Familiarity with standard computational and statistical methods to analyze high-throughput and high-content biological datasets (e.g. Tuba-seq, Perturb-seq, WES, scRNA-seq, spatial transcriptomics/proteomics, etc).
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A “no task too big or too small” and “jump in where needed” mindset befitting of a small team starting up within a larger organization.
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Excellent communication, collaboration, and project management skills.
Preferred qualifications:
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Oncology experience, particularly working with preclinical cancer model data (in vivo drug efficacy and genetic biomarkers) and clinical cancer data (clinicogenomics data), including an understanding of cancer genes, genomic alterations, gene complexes/pathways, and clinical outcomes.
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Ability to generate custom bioinformatics pipelines for the analysis of next-generation sequencing data.
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Experience synthesizing statistical/biological insights from multiple independent studies (i.e. meta-analysis) and from diverse experimental readouts (e.g. summarizing conclusions from joint in vitro, in vivo, and clinical data of various types).
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Familiarity with software engineering best practices including around data modeling, pipelining, cloud computing, storage, and provenance.
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Familiarity with Python SDKs/APIs for CRUD (Create, Read, Update, Delete) access into Lab Information Management Systems (LIMS) to manage and analyze structured biological study (meta)data.
Revolution Medicines currently requires that all personnel and visitors to its offices be fully vaccinated against COVID-19. This role will require that the employee meet with company employees and work from the company’s offices. Given that these essential functions of the role must be performed on-site, this position requires full COVID-19 vaccination, subject to applicable law.
The expected salary range for this role is $195,000 to $245,000. An individual’s position within the range may be influenced by multiple factors, including skills and experience in role, overall performance, individual impact and contributions, tenure, and market dynamics. Base salary is one part of the overall total rewards program at RevMed, which includes competitive cash compensation, robust equity awards, strong benefits, and significant learning and development opportunities.
Revolution Medicines is an equal opportunity employer and prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, disability, marital status, medical condition, and veteran status.
Revolution Medicines takes protection and security of personal data very seriously and respects your right to privacy while using our website and when contacting us by email or phone. We will only collect, process and use any personal data that you provide to us in accordance with our CCPA Notice and Privacy Policy. For additional information, please contact [email protected].
