Job Description

Contact: Neisha Camacho/Terra Parsons - teamnt@penfieldsearch.com
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 (EU and China exposure preferred)

• 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

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

Additional Requirements

• Hands-on experience with LSAF - Life Sciences Analytical Framework

• 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

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