Associate Director, Real-World Analytics Programing

Revolution Medicines

1d•$177,000 - $221,000•Remote

About The Position

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. 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.

Requirements

BS/MS degree in Statistics, Biostatistics, Computer Science, Data Science, Epidemiology, Mathematics, or a related quantitative discipline.
8+ years of statistical programming experience in pharmaceutical, biotechnology, CRO, healthcare research, or related settings for Senior-level candidates.
Demonstrated experience independently leading programming activities for observational research, HEOR, epidemiology, or real-world evidence studies.
Strong SAS or R programming experience required, including experience developing analysis datasets and study deliverables from complex healthcare data sources.
Experience with SQL required and working knowledge of R or other programming language for data manipulation, analytics support, and quality control activities preferred.
Experience developing analysis datasets, tables, listings, and figures (TLFs) from complex healthcare or real-world data sources.
Experience working with large healthcare databases, including claims, EHR, genomic, registry, or linked clinical-genomic datasets.
Strong understanding of observational research workflows and implementation of study specifications.
Demonstrated ability to manage multiple projects simultaneously while meeting timelines and quality expectations.
Excellent written and verbal communication skills.
Strong analytical, problem-solving, and organizational skills with exceptional attention to detail.
Thrives in a collaborative team environment and is motivated to drive high-quality technical execution and continuous process improvement.

Nice To Haves

Oncology drug development or oncology real-world evidence experience.
Experience working with major oncology or healthcare real-world data assets, such as Flatiron Health, ConcertAI, COTA, Tempus, Guardant, Merative MarketScan, Komodo, Optum, TriNetX, or similar datasets.
Experience supporting HTA, market access, indirect treatment comparison (ITC), external control arm, or regulatory-grade real-world evidence studies.
Experience developing reusable programming frameworks, standardized macros, and validation tools.
Experience with Git, version control systems, and reproducible analytics workflows.
Experience working in a fast-paced and growing biotechnology organization.

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.
Supports a broad range of HEOR/RWE studies, including treatment pattern analyses, burden of illness studies, comparative effectiveness and safety studies, natural history studies, and external control arm analyses.
Implements and validate analytic methods specified in study protocols and SAPs, including survival analyses, propensity score approaches, confounding adjustment methods, and sensitivity analyses.
Works with multiple oncology-focused real-world data assets, including claims, electronic health records (EHR), genomic testing databases, and linked clinical-genomic datasets.
Assesses data quality, completeness, and fitness-for-purpose and provide recommendations regarding data availability and implementation feasibility.
Independently performs quality control and validation activities for programming code, analysis datasets, and study outputs.
Develops and maintains technical documentation, programming specifications, reusable code, macros, and reproducible analytic workflows for assigned studies.
Collaborates closely with HEOR/RWE scientists, Data Scientists, and Biostatisticians to ensure accurate implementation of study methodologies and successful delivery of study objectives.
Communicates programming assumptions, implementation decisions, timelines, and risks to study teams and stakeholders.
Contributes to real-world programming standards, quality frameworks, and best practices.
Provides technical guidance to junior programmers as appropriate.
Stays current with evolving industry best practices, technologies, and regulatory expectations related to oncology real-world data and evidence generation.