Associate Director, Real-World Analytics Programing

Role overview A lead statistical programming role within a real-world evidence and health economics function supporting oncology research. The position independently manages programming activities for observational studies from kickoff through final deliverables, translating study designs into reproducible analytic solutions. The work spans a broad portfolio including treatment pattern analyses, burden of illness studies, comparative effectiveness and safety research, natural history studies, and external control arm analyses.

Responsibilities - Serve as the lead statistical programmer for assigned real-world studies, managing activities from initiation through final delivery. - Lead programming deliverables including analysis dataset specifications, table, listing, and figure shells, validation plans, programming documentation, and final study outputs. - Review study protocols, statistical analysis plans, and analysis specifications to ensure accurate implementation of requirements. - Develop, validate, and maintain SAS, SQL, R, Python, and other programming solutions for cohort construction, endpoint derivation, analysis dataset creation, and study deliverables. - Generate and quality-control analysis datasets, tables, listings, and figures in accordance with protocols, SAPs, and internal standards. - Implement analytic methods specified in protocols and SAPs, including survival analyses, propensity score approaches, confounding adjustment methods, and sensitivity analyses. - Work with oncology-focused real-world data assets such as claims, electronic health records, genomic testing databases, and linked clinical-genomic datasets. - Assess data quality, completeness, and fitness-for-purpose and communicate recommendations, assumptions, timelines, and risks to study teams. - Contribute to real-world programming standards, quality frameworks, and reusable code, providing technical guidance to junior programmers as needed.

Requirements - Bachelor's or advanced degree in statistics, biostatistics, computer science, or a related quantitative field; master's preferred. - Significant experience as a lead statistical programmer supporting real-world evidence, health economics and outcomes research, or observational studies. - Strong proficiency in SAS, SQL, R, and Python for reproducible analytics. - Demonstrated experience with study documentation including protocols, SAPs, and analysis specifications. - Experience working with claims, electronic health record, or genomic datasets. - Familiarity with Git or other version control systems and reproducible analytics workflows.

Nice to have - Experience developing reusable programming frameworks, standardized macros, and validation tools. - Experience working in a fast-paced and growing biotechnology organization.

Benefits and work setup - Remote-eligible position with a published base pay range of $177,000 to $221,000 USD, plus competitive equity awards and benefits. - Opportunities for learning and development as part of a broader total rewards program.