The posting, in Revolution Medicines's own words
archived Oct 14, 2026Revolution 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:
The Director Statistician Real‑World Analytics will provide statistical leadership for real-world evidence (RWE), epidemiology, health economics and outcomes research (HEOR) studies supporting Revolution Medicines' oncology development programs. This individual will serve as the statistical lead for observational research studies across the drug development lifecycle, partnering closely with HEOR/RWE scientists, real-world analytics programmers, clinical development, medical affairs, regulatory affairs, safety, market access, and other stakeholders. The role will be lead the development of statistical analysis plans (SAPs) for observational research studies including statistical methodology and causal inference strategy when appropriate. The successful candidate will have deep expertise in oncology real-world data (RWD), comparative effectiveness research, external control arm methodologies, and advanced causal inference approaches, with the ability to generate evidence supporting regulatory, payer, HTA, and scientific decision-making.
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Responsibilities:
Serves as the statistical lead for real-world studies from concept development through final study delivery. Partners with HEOR/RWE scientists and cross-functional stakeholders to design observational studies addressing clinical, regulatory, market access, and scientific objectives. Collaborates with HEOR/RWE scientists and study teams to develop study protocols and lead development of statistical analysis plans (SAPs) and statistical analysis specifications for HEOR/RWE, healthcare resource utilization (HCRU), epidemiology, and post-approval safety studies, as well as comparative effectiveness and safety studies, and when appropriate provide statistical rationale for implementation of external control arm studies, all using real-world data. Studies are used to inform internal stakeholder groups (e.g., clinical development) as well as external groups (e.g., regulatory decision making). Collaborates with HEOR/RWE scientists to design statistical thinking to evaluate indirect treatment comparison (ITC), matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and network meta-analysis (NMA) approaches where appropriate. Partners with HEOR/RWE and cross-functional stakeholders to develop evidence strategies supporting HTA, JCA, payer, reimbursement, and access decision-making. Applies advanced causal inference methodologies, including propensity score methods, inverse probability weighting, matching, stratification, doubly robust approaches, marginal structural models, and target trial emulation frameworks. Develops strategies to address confounding, selection bias, missing data, immortal time bias, and other common challenges in observational research. Provide statistical oversight of analysis implementation performed by real-world statistical programmers. Interprets study findings and communicate results to multidisciplinary stakeholders. Contributes to publications, scientific presentations, regulatory interactions, payer engagements, and HTA submissions. Stays current with evolving regulatory guidance and methodological advances related to oncology RWE, causal inference, and evidence generation. Provide statistical leadership and methodological guidance for studies utilizing claims, EHR, genomic, registry, and linked clinical-genomic datasets. Evaluates data fitness-for-purpose, study feasibility, endpoint availability, follow-up completeness, and potential sources of bias across major oncology and healthcare real-world data assets, including Flatiron Health, ConcertAI, COTA, Tempus, Guardant, Merative MarketScan, Komodo, Optum, TriNetX, and similar data sources.