Associate Director, Real-World Analytics Programing

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Associate Director, Real-World Analytics Programing based in United States. This is a senior statistical programming role supporting complex real-world analytics and evidence-generation studies in oncology. You will serve as a lead programmer, owning programming activities from study initiation through final deliverables and ensuring that analyses are accurate, reproducible, and aligned with study requirements. The role works closely with HEOR/RWE scientists, Data Scientists, Biostatisticians, and other cross-functional partners to translate study protocols and statistical analysis plans into high-quality outputs. You will work with large and complex healthcare data sources, including claims, electronic health records, genomic data, and linked clinical-genomic datasets. The position combines hands-on technical programming with study leadership, quality oversight, and methodological implementation. It offers the opportunity to contribute to impactful real-world evidence programs while helping advance programming standards, reusable workflows, and analytical best practices. Accountabilities: • Serve as the lead statistical programmer for assigned real-world analytics studies, independently managing programming activities from study kickoff through final study delivery and reporting. • Lead programming deliverables across the study lifecycle, including analysis dataset specifications, TLF shells, validation plans, programming documentation, analysis datasets, and final study outputs. • Review study protocols, statistical analysis plans, analysis specifications, and study concept documents to ensure programming solutions accurately reflect study objectives and methodological requirements. • Translate study designs and analytical specifications into efficient, scalable, maintainable, and reproducible programming solutions using SAS, SQL, R, Python, and other appropriate technologies. • Develop, validate, and maintain programming solutions for cohort construction, endpoint derivation, analysis dataset creation, and production of tables, listings, and figures. • Support a broad range of HEOR and real-world evidence studies, including treatment pattern analyses, burden of illness studies, comparative effectiveness and safety research, natural history studies, and external control arm analyses. • Implement and validate analytical methods specified in study protocols and SAPs, including survival analyses, propensity score approaches, confounding adjustment techniques, and sensitivity analyses. • Work with complex oncology-focused real-world data assets, including claims, EHRs, genomic testing databases, registries, and linked clinical-genomic datasets, while assessing their quality, completeness, and fitness for purpose. • Independently perform quality control and validation activities for programming code, analytical datasets, and study outputs, ensuring high standards of accuracy and reproducibility. • Develop and maintain technical documentation, programming specifications, reusable code, macros, validation tools, and reproducible analytical workflows for assigned studies. • Collaborate closely with HEOR/RWE scientists, Data Scientists, and Biostatisticians to ensure methodologies are implemented accurately and study objectives are delivered effectively. • Communicate programming assumptions, implementation decisions, timelines, dependencies, and potential risks clearly to study teams and stakeholders. • Contribute to real-world programming standards, quality frameworks, and technical best practices while providing guidance and mentorship to junior programmers as appropriate. • Stay current with evolving industry practices, technologies, and regulatory expectations related to oncology real-world data and evidence generation. Requirements: • Hold a BS or MS degree in Statistics, Biostatistics, Computer Science, Data Science, Epidemiology, Mathematics, or another relevant quantitative discipline. • Bring 8+ years of statistical programming experience in pharmaceutical, biotechnology, CRO, healthcare research, or related environments, with substantial experience operating at a senior level. • Demonstrate experience independently leading programming activities for observational research, HEOR, epidemiology, or real-world evidence studies from initiation through final deliverables. • Have strong SAS or R programming expertise, including experience developing analytical datasets and study deliverables from complex healthcare data sources, together with required SQL proficiency. • Demonstrate working knowledge of R or another programming language for data manipulation, analytics support, and quality control activities where applicable. • Bring experience developing analysis datasets, tables, listings, and figures from large and complex healthcare or real-world data sources. • Have experience working with healthcare databases such as claims, EHR, genomic, registry, or linked clinical-genomic datasets, with oncology data experience strongly preferred. • Possess a strong understanding of observational research workflows and the ability to accurately translate protocols, SAPs, and analytical specifications into programming solutions. • Demonstrate strong analytical, problem-solving, organizational, and project management skills, with exceptional attention to detail and the ability to manage multiple studies and competing priorities. • Have excellent written and verbal communication skills and the ability to explain technical decisions, assumptions, risks, and timelines to cross-functional stakeholders. • Be comfortable working collaboratively in a fast-paced environment while maintaining high standards for technical execution, quality, reproducibility, and continuous improvement. • Experience with oncology drug development or oncology real-world evid