Overview As a Statistical Modeler in the pharmacometrics group, you will build exposure-response and drug-drug interaction models to support clinical decision making in oncology. You'll apply state of the art statistical and machine learning methods to reveal exposure-response relationships and guide dose finding within the RAS targeted portfolio. You'll interpret results for cross functional teams and enable data driven development decisions. This hybrid role places you in the San Francisco Bay Area, contributing to Revolution Medicines' mission to advance RAS addicted cancers with rigorous quantitative science.
Compensation / Benefits- competitive cash compensation
- robust equity awards
- strong benefits
- learning and development opportunities
Responsibilities- Build standard ER and DDI models for clinical studies
- Develop and apply advanced statistical and ML methods to link exposure and response
- Interpret ER results to inform dose-finding decisions
- Support pharmacometrics functions within the department
Key requirements- Ph.D. in Statistics, Biostatistics, Mathematics or related field
- Biotech/pharmaceutical internship or work experience
- Experience applying statistical methods to real world datasets
- Knowledge of hierarchical models and Bayesian inference with real world data
- Proficiency with GLMs, survival models, and mixed effects models
- Proficiency in R and/or SAS programming
- ability to present complex concepts to diverse audiences
- strong communication of results to biotech/pharma stakeholders
- collaborative mindset in cross functional teams
- Hierarchical models
- Bayesian inference
- Generalized linear models (GLMs)
