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:
We are seeking a QSP modeling & simulation scientist to be part of the Nonclinical Development and Clinical Pharmacology (NDCP) organization. This position will be responsible for developing, validating, and executing modeling projects with a focus on mechanistic PBPK-QSP mathematical models for small molecule programs to increase mechanistic understanding of compound PK behavior and drug distribution, pharmacological effects on RAS targets, support clinical translation, and drive future discovery and development efforts. As a Quantitative Systems Pharmacologist, you will:
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Develop, validate, execute, and refine quantitative systems pharmacology (QSP) models, minimal physiologically based pharmacokinetic (PBPK) models, semi-mechanistic PK/PD models, and tumor growth models to support development and discovery phase projects including next-generation inhibitor design and assessment of combination potential.
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Propose and perform in silico simulations to answer complex mechanistic questions, create data visualizations to effectively communicate modeling results to a wide-ranging audience, and devise strategies to improve model outputs.
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Survey the related literature to understand key physiological and biological processes, abstract the basic mechanistic elements, identify the relevant data, and summarize assumptions to be incorporated into existing or new PBPK-QSP models.
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Propose new mechanistic in vitro and in vivo experiments to test model assumptions and structure. Provide in silico support for preclinical translation including clinical efficacious doses/exposure projection, potential combination dosing regimens with other cancer therapeutics.
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Work collaboratively with other functions to build internal infrastructure supporting data transfer and quality control.
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Document contributions, including assumptions, mathematical models, data analyses, and data visualizations, to be shared with other scientists or used for archival purposes.
Required Skills, Experience and Education:
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A Ph.D. in a quantitative discipline (systems pharmacology, computational biology, engineering, mathematics, physics, etc.) and 0-2 years of industry experience is desired.
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Strong understanding of the principles and limitations of mathematical modeling, pharmacokinetic models, pharmacodynamic models, and quantitative systems pharmacology/biology models.
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Proficiency in mathematical and computational methods including ordinary differential equations (ODEs), nonlinear systems, statistics, optimization, and parameter inference.
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Proven record developing, calibrating, and validating dynamical system models in pharmacological and biological systems.
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Demonstrable hands-on experience with programming languages used in scientific computing, such as MATLAB, Python, Julia, and R.
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Capable of working proactively and independently to deliver high–quality modeling results in a timely manner.
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Able to effectively communicate modeling assumptions, limitations, and simulation results to non-specialist and specialist audiences.
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A critical thinker and team player who can work cross-functionally with others.
Preferred Skills:
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Experience with diverse dynamical system methods like ODE-based, PDE-based, nonlinear mixed effects, agent-based, Markov, Boolean, etc.
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Experience with integrating large data sets into QSP.
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Experience with agentic coding workflows such as Copilot, Cursor, Codex, and Claude Code.
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Experience with data-driven methods such as ML-based predictive regression models and physics-informed neural network models.
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Experience with modeling software such as SimBiology, NONMEM, Pheonix WinNonlin, Monolix, Simcyp designer, etc.
#LI-Hybrid #LI-CT1
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