Scientist II, Cancer Pharmacology, Translational Research

Revolution Medicines•Redwood City, CA

1d•$128,000 - $160,000•Hybrid

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. The Opportunity: As a Scientist II in the Translational Research team, within the Biology Department, you will: As a member of a cross-functional quantitative modeling group, collaborate with team members to develop, validate and refine physiologically-based PK (PBPK), quantitative systems pharmacology (QSP), and semi-mechanistic PK/PD models to support discovery and development-phase projects. Seek and apply innovative modeling approaches to understand tumor resistance to RAS(ON) inhibitors and build in silico models, propose mechanistic in vitro and in vivo experiments to test model assumptions and structure. Design and oversee in vivo and ex vivo studies (internally and via CROs), develop and maintain high-quality protocols and documentation, and present results at internal forums and scientific conferences.

Requirements

Ph.D. in pharmacology or cancer biology, or a quantitative discipline (pharmaceutical sciences, mathematics, systems pharmacology, computational biology, etc.) with direct relevance to drug development.
2+ years of relevant postdoc or industry experience in cancer drug discovery and development.
Demonstrated experience in in vivo experiments with oncology disease models.
Solid PK/PD knowledge and prior experience with PK/PD data collection and analysis.
A strong theoretical understanding of the core concepts, assumptions, and limitations associated with PK/PD mathematical modeling
Rigorous, quantitative and detail-oriented with proven excellence in experimental design, data analysis, data management, and presentation.
Excellent written and verbal communication skills.
Able to effectively communicate modeling assumptions, limitations, and simulation results to non-specialist and specialist audiences.
Demonstrated ability to multi-task, prioritize options, anticipate challenges, and execute on goals as a member of an interdisciplinary team is extremely important.
Thrives in a collaborative team setting and is driven by a desire to be innovative in a dynamic and fast-paced environment.

Nice To Haves

An understanding of preclinical to clinical translational concepts desired.
Strong track record of research productivity as evidenced by high-quality, impactful publications.
Experience with development, calibration, and validation of ordinary differential equation (ODE) and non-linear mixed effect (NLME) models.
Hands on experience with modeling software (Simbiology, Phoenix WinNonlin, NONMEM, etc.) and PBPK model development.

Responsibilities

As a member of a cross-functional quantitative modeling group, collaborate with team members to develop, validate and refine physiologically-based PK (PBPK), quantitative systems pharmacology (QSP), and semi-mechanistic PK/PD models to support discovery and development-phase projects.
Seek and apply innovative modeling approaches to understand tumor resistance to RAS(ON) inhibitors and build in silico models, propose mechanistic in vitro and in vivo experiments to test model assumptions and structure.
Design and oversee in vivo and ex vivo studies (internally and via CROs), develop and maintain high-quality protocols and documentation, and present results at internal forums and scientific conferences.

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