Associate Clinical SAS Programmer/Junior Statistician

Job Responsibilities

• Dataset Development & Mapping: Develop, validate, and maintain SAS programs to create SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model) datasets from raw clinical databases.
• Create and review dataset specifications based on the protocol, and case report forms (eCRFs).
• Draft and implement Statistical Analysis Plan (SAP) and create mock-up shells for Tables, Listings, and Figures (TFLs) under senior guidance.
• TFL Generation: Program, test, and validate Tables, Listings, and Figures (TFLs) required for Conferences, Publications, Clinical Study Reports (CSRs), Interim Analysis, Data Monitoring Committees (DMC), and safety monitoring.
• Perform routine statistical analyses (such as descriptive statistics, t-tests, ANOVA, and basic regression modeling) using SAS.
• Standards & Compliance: Ensure strict compliance with CDISC standards (SDTM/ADaM) and internal standard operating procedures (SOPs).
• Validation & Quality Control: Perform independent double-programming to validate high-risk datasets and statistical outputs, ensuring 100% accuracy and reproducibility of clinical trial results.
• Reporting and Interpretation: Assist in writing the statistical methods and results sections for Clinical Study Reports (CSRs), abstracts, and manuscripts. Prepare clear, concise summaries of statistical findings for internal project teams and clinical investigators.
• Effectively translate complex statistical requests from principal investigators and biostatisticians into programmatic solutions.
• Manage project deliverables to ensure they remain within the timeline and budget
• Develop timelines for programming.
• Participate in and lead process improvement activities within the department and cross functionally, including SOP development
• Work with other clinical research team members on study start-up activities including the development and review of case report forms.
• Manage multiple timelines, protocol amendments, and urgent data requests simultaneously.
• Meticulous approach to data quality and validation code.
• Maintain effective working relationships with principal investigators