Lead Statistical Programmer - Global Studies (Remote)

RemoteWorker US
Fairfield, CT, USA

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

ID

a1e85d83f4bfaf121bafa8ab73891f4d