Senior Manager Statistical Programming

Senior Manager, Statistical Programming 

Position Summary 

The Senior Manager, Statistical Programming will provide hands-on statistical programming leadership and support for assigned clinical studies and collaborate with Biostatistics, Clinical Data Management, Clinical Operations, Safety, and other study functions. This role is responsible for the design, development, validation, and quality control of SAS programs used to access, transform, analyze, report, and submit clinical trial data. 

The Senior Manager will serve as lead programmer for assigned studies, provide technical guidance to project teams, and ensure that programming deliverables are accurate, consistent, and completed within established timelines. 

Essential Duties and Responsibilities 

• Serve as lead study programmer and support review of statistical analysis plans, development of mock TFLs, programming dry runs, and production of draft and final TFLs. 

• Develop, validate, and maintain SDTM and ADaM datasets, programming specifications, metadata, and support documentation. 

• Program and validate tables, figures, and listings for efficacy, safety, pharmacokinetic, pharmacodynamic, and clinical outcome assessments. 

• Design and develop efficient SAS programs, macros, and reusable utilities using Base SAS, SAS Macro, SAS/STAT, SAS/GRAPH, and ODS. 

• Transform and integrate data from EDC systems, IRT systems, laboratories, eCOA platforms, and other external sources. 

• Perform quality control reviews of SAS programs, datasets, specifications, logs, and outputs produced by other programmers. 

• Investigate and resolve complex data, derivation, programming, and reporting issues with limited supervision. 

• Support database locks, safety updates, clinical study reports, publications, and other study deliverables. 

• Support regulatory submissions through preparation and validation of SDTM, ADaM, define.xml, SDRG, ADRG, annotated CRFs, and related submission components. 

• Review and resolve CDISC and submission validation findings. 

• Apply MedDRA, WHODrug, controlled terminology, and other applicable industry standards. 

• Maintain clear traceability from source data through SDTM, ADaM, and final statistical outputs. 

• Contribute to programming standards, templates, automation, and process improvement initiatives. 

• Provide technical guidance and peer review to less experienced programmers and contribute to their technical development as needed. 

Knowledge and Skill Requirements 

• Master’s degree in Statistics, Mathematics, Computer Science, or a related discipline. 

• Minimum of 7 years of statistical programming experience in a pharmaceutical, biotechnology, or CRO environment. 

• Strong hands-on SAS programming skills and experience with clinical trial reporting. 

• Strong knowledge of CDISC standards, including SDTM, ADaM, metadata, controlled terminology, data flow, and FDA electronic submission requirements. 

• Experience independently developing and validating analysis datasets, programming specifications, and TFLs. 

• Demonstrated experience serving as a lead programmer for Phase 2 or Phase 3 clinical studies, with the ability to independently manage study-level programming deliverables. 

• Good understanding of clinical development, medical data, and common safety and efficacy domains. 

• Strong analytical, problem-solving, debugging, and quality-control skills. 

• Ability to manage multiple study deliverables, adapt to changing priorities, and work effectively with cross-functional teams. 

• Strong written and verbal communication skills. 

• Experience in autoimmune disease, neurology, or rare-disease clinical development is preferred. 

• Working knowledge of R or other programming technologies is desirable. 

 

The salary range for this Senior Manager Statistical Programming position is expected to be between $190,000 and $205,000 per year.  Individual pay may vary based on multiple factors including but not limited to relevant job-related skills, experience, education or training, market factors, and work location.