Statistical Programmer

Job Title: Statistical Programmer – Clinical Data (SAS/R) Location : Pan India Experience : 3 – 12 years Industry : Pharmaceuticals / Biotechnology / Clinical Research

About the Role We are looking for an experienced Statistical Programmer to oversee programming activities for clinical trials, regulatory submissions, and integrated analyses. This role involves leading a team, ensuring high-quality deliverables, and developing efficient programming standards to support drug development.

Key Responsibilities Lead statistical programming activities for a compound or therapeutic area. Manage, mentor, and guide a team of statistical programmers. Create and validate analysis data sets (ADaM), tables, listings, and figures (TFLs) following CDISC standards. Develop and validate Integrated Summary of Safety (ISS) and Integrated Summary of Efficacy (ISE) reports. Ensure accuracy and consistency across clinical studies and integrated data. Develop, test, and implement reusable standard programs and SAS macros to automate routine tasks. Prepare reviewer guides, data definition documents, and other materials for regulatory filings. Collaborate with cross-functional teams including Statisticians, Clinical Data Management, Medical Writing, Regulatory, and Clinical Operations. Accurately estimate programming efforts and ensure timely project delivery.

Required Skills Strong proficiency in SAS programming; experience with R preferred. In-depth knowledge of CDISC standards (ADaM, SDTM). Experience with regulatory submissions (FDA, EMA) and clinical study lifecycle. Knowledge of therapeutic areas such as Oncology, Immunology, Neuroscience. Strong communication and leadership skills to guide teams and manage stakeholders. Ability to handle multiple priorities and deliver high-quality results under deadlines.

Why Join Us? Work on cutting-edge clinical trials and regulatory submissions. Collaborate with a team of top-tier professionals. Opportunity to develop advanced programming frameworks and drive innovation in data analytics.