Principal Statistical Programmer - Global Studies- REMOTE

Principal Statistical Programmer•Global Studies•REMOTE Penfield Search Partners Fairfield, CT Job Details Full-time | Contract 2 hours ago Qualifications Data quality checks Biostatistics SAS Data imputation Data quality assurance SAS language Data quality management ICH guidelines Clinical program data analysis CDISC standards Document quality checks Clinical quality assurance standards Clinical data analysis

Full Job Description Salary:

Market related

Reference:

JOB-6174

Contact:

Neisha Camacho/Terra Parsons•No 3rd party candidates We are seeking a highly experienced Statistical Programmer to lead programming activities across global clinical studies. This role operates beyond executional programming, with responsibility for oversight of CRO deliverables, validation of outputs, and end-to-end accountability for statistical programming packages. Key Responsibilities Lead statistical programming activities across global studies Serve as primary programming lead in collaboration with Biostatistics Develop, review, and validate SDTM and ADaM datasets in accordance with CDISC standards Review specifications and proactively challenge inconsistencies in protocols, SAPs, and dataset definitions Validate program outputs and ensure accuracy, quality, and regulatory compliance Provide oversight and guidance to CRO partners, consolidating and communicating feedback effectively Manage timelines, delivery packages, and milestone commitments Contribute to continuous improvement of programming processes and standards Core Requirements Strong expertise in CDISC standards, including ADaM and SDTM Demonstrated experience reviewing specs and ensuring high-quality, submission-ready deliverables Working experience in LSAF environment Experience validating CRO programming deliverables Ability to operate with increased performance accountability and ownership Strong CRO-facing communication and collaboration skills Proven ability to manage multiple global studies simultaneously Additional Requirements Practical experience with multiple imputation (MI), particularly under Missing at Random (MAR) assumptions Familiarity with the estimands framework (ICH E9 R1) and managing intercurrent events (ICEs) within ADaM domains using various strategies Qualifications Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, or related field 5+ years of SAS programming experience within pharmaceutical/biotech Strong understanding of statistical methods used in clinical trial analysis

Knowledge of Good Programming Practices and GCP Preferred:

Experience with R programming