Medical Analytics and Exploratory Data Science

Revolution Medicines is a global, commercial-state oncology company dedicated to discovering, developing and delivering innovative medicines for patients with RAS-addicted cancers. Leveraging its differentiated RAS(ON) tri-complex inhibitor platform, the company is advancing a broad, integrated portfolio of oral RAS(ON) inhibitors designed to directly target the active, cancer-driving state of RAS. Founded on rigorous scientific inquiry and a willingness to challenge long-held assumptions, Revolution Medicines is committed to changing the trajectory of disease for patients with RAS-addicted cancers worldwide.

Our people are united by a shared way of working: follow the science, challenge assumptions, act with urgency and hold ourselves to a high standard of rigor—all in service of patients.

The Opportunity:

We are seeking a highly capable Associate Director of Biostatistics to join our Medical Analytics and Exploratory Data Science Biostatistics group within our Biostatistics organization. This role will play a critical part in the design, analysis, and interpretation of exploratory data analyses, scientific publications, real-world evidence (RWE), post-marketing research, and health economics and outcomes research (HEOR) studies. The successful candidate will serve as a key statistical contributor and emerging leader, partnering closely with cross-functional teams to deliver high-quality, data-driven insights that support scientific understanding and evidence generation.

• Lead statistical design, analysis, and interpretation for exploratory data analyses using existing clinical trial data, real world data studies, post-marketing research, and HEOR projects.

• Partner closely with other subfunctions within quantitative sciences and with cross-functional teams, including clinical development, medical affairs, safety, statistical programming, regulatory affairs and commercial, to execute evidence generation plans.

• Apply appropriate statistical methodologies, including survival analysis, machine learning, and casual inference approaches, to address complex scientific and medical questions in oncology.

• Contribute to the development of analysis plans, technical specifications, and interpretation of results under general direction from senior statistical leadership.

• Support cross-functional evidence generation planning by providing statistical input into study design, feasibility, and analysis strategies.

• Review and oversee statistical deliverables produced by internal programmers or external vendors/contractors to ensure scientific quality and consistency with standards.