Senior Statistical Programmer

Job Title: Senior Statistical Programmer (RWD) Locations: United Kingdom and Ireland Shift Schedule: 8:00 AM - 5:00 PM local time (based on candidate’s country) Duration: Up to 2 years Job Overview: We are seeking an experienced Senior Statistical Programmer with strong expertise in statistical programming and Real World Evidence (RWE) studies. The ideal candidate will collaborate with biostatisticians, data managers, and clinical teams to provide high-quality programming support for the design, analysis, and reporting of interventional and observational studies within the pharmaceutical industry. Key Responsibilities: Develop, validate, and maintain SAS programs to generate datasets, tables, listings, and figures (TLFs) for clinical and observational studies. Support programming deliverables across Phase IV, Medical Affairs, RWE, and HEOR studies. Ensure adherence to CDISC standards (SDTM and ADaM) for data structure and analysis. Collaborate with biostatisticians to implement

statistical analysis plans (SAPs) and provide programming input during study design. Perform quality control (QC) checks and ensure accuracy and consistency of deliverables. Manage and manipulate Real World Data (RWD) for use in RWE analyses. Apply advanced analytical and statistical methods, including propensity score matching, causal inference, and mixed-effects modeling. Ensure documentation, programming specifications, and deliverables comply with regulatory and industry standards. Work collaboratively across multiple functional teams to meet timelines and project goals. Core Essential Skill Sets: Master’s or PhD degree in Biostatistics, Statistics, Computer Science, or a related field. Proven experience in the pharmaceutical or CRO industry, providing programming and analytical support for clinical or observational studies. Experience working on Phase IV, Medical Affairs, Real World Evidence (RWE), or Health Economics and Outcomes Research (HEOR) studies. Hands-on experience in SAS programming; experience in R is an advantage. Experience with CDISC standards, including SDTM and ADaM datasets. Experience with Real World Data (RWD) and RWE methodologies, including propensity score analysis and causal inference. Familiarity with advanced statistical models, such as mixed-effects models for repeated measures, and exposure to Machine Learning (ML) techniques is desirable.