Associate Director, Real-World Analytics Programming

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:

This position will serve as the lead statistical programmer for assigned real-world analytics and programming studies, independently managing programming activities from study initiation through final deliverables. The individual will be responsible for translating study protocols, statistical analysis plans (SAPs), and analysis specifications into high-quality, reproducible programming solutions and study outputs.

The Real-World Analytics Programmer will partner closely with HEOR/RWE scientists, data scientists, biostatisticians, and other cross-functional stakeholders to support the successful execution of observational research studies. The role requires strong technical expertise, attention to detail, and the ability to manage multiple studies and competing priorities in a fast-paced environment.

Responsibilities: 

• Serves as the lead statistical programmer for assigned real-world studies and manage programming activities from study kickoff through final delivery.

• Leads programming deliverables from study initiation through final report, including analysis dataset specifications, TLF shells, validation plans, programming documentation, and final study outputs.

• Reviews and understand study-specific documents including protocols, SAPs, analysis specifications, and study concept documents to ensure accurate implementation of study requirements.

• Translates study designs and analytic specifications into efficient, scalable, and reproducible programming solutions.

• Independently develops, validates, and maintains SAS, SQL, R, Python, and other programming solutions for cohort construction, endpoint derivation, analysis dataset creation, and study deliverables.

• Generates and quality-control analysis datasets, tables, listings, and figures (TLFs) in accordance with study protocols, SAPs, analysis specifications, and internal standards.