Pharmacometrics Scientist II - Bayesian ER Modeling

Revolution Medicines; San Francisco (CA); Hybrid; USD 132,000 - 199,000; Full time

Job summary

Revolution Medicines seeks a PhD‑level statistician to support pharmacometrics in our oncology-focused team. You will build exposure–response and DDI models, apply advanced statistical and machine‑learning methods, and interpret results to guide dose selection in clinical studies.

Hybrid work arrangement; employees reside in the San Francisco Bay Area and may work remotely two days per week. Base pay is disclosed and total rewards include equity and comprehensive benefits.

Qualifications

• Ph.D. or equivalent in Statistics, Biostatistics, Mathematics or related field.
• Experience applying statistical methods to real-world biotech or pharma datasets.
• Ability to present complex results to diverse audiences in biotech/pharma.

Responsibilities

• Be a statistical modeler and support pharmacometrics in the department.
• Build standard statistical models for exposure–response and drug–drug interaction analyses in clinical studies.
• Develop and apply state‑of‑the‑art statistical and ML methods to identify exposure‑response associations.
• Interpret ER results to guide dose‑finding decisions.

Skills

Bayesian inference

Hierarchical models

GLMs

Survival models

Mixed-effects models

Data analysis

Education

Ph.D. or equivalent in Statistics/Biostatistics/Mathematics

Tools

R

SAS

Job description

Revolution Medicines seeks a PhD‑level statistician to support pharmacometrics in our oncology-focused team. You will build exposure–response and DDI models, apply advanced statistical and machine‑learning methods, and interpret results to guide dose selection in clinical studies.

Hybrid work arrangement; employees reside in the San Francisco Bay Area and may work remotely two days per week. Base pay is disclosed and total rewards include equity and comprehensive benefits.