Director, Statistician Real-World Analytics

Revolution Medicines

2d•$201,000 - $251,000•Remote

About The Position

Revolution Medicines is a global, commercial-stage 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 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 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.

Requirements

PhD or MS in Biostatistics, Statistics, Epidemiology, Health Economics, Data Science, or related quantitative discipline.
10+ years of relevant experience.
Demonstrated experience leading statistical strategy for observational research, comparative effectiveness studies, HEOR, HCRU, epidemiology, or real-world evidence studies.
Strong understanding of causal inference methodology and observational study design.
Experience developing study protocols, SAPs, and statistical specifications.
Experience designing and evaluating propensity score methodologies and confounding adjustment approaches.
Experience interpreting and communicating observational research findings to scientific and non-scientific audiences.
Experience working with large healthcare databases including claims, EHR, genomic, registry, and linked datasets.
Strong knowledge of regulatory and methodological guidance relevant to RWE, including guidance and best practices from FDA, EMA, ISPOR, ISPE, NICE, or related organizations.

Nice To Haves

Oncology drug development experience.
Experience supporting external control arm development.
Experience supporting regulatory-grade real-world evidence studies.
Experience supporting HTA, JCA, payer, or market access evidence generation.
Experience conducting or overseeing indirect treatment comparisons (ITC), MAIC, STC, or network meta-analyses.
Experience designing target trial emulation studies.
Experience working with major oncology and healthcare real-world data assets, including Flatiron Health, ConcertAI, COTA, Tempus, Guardant, Komodo, Optum, TriNetX, and Merative MarketScan.
Experience designing and evaluating oncology-specific real-world endpoints, including real-world overall survival, real-world progression-free survival, treatment duration, time to treatment discontinuation, response-based endpoints, and healthcare resource utilization measures.
Publication record in oncology, epidemiology, HEOR, or real-world evidence.
Experience working in a fast-paced biotechnology environment.

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.