Lead Statistical Programmers

Tech Observer, global CRO, is seeking an experienced Lead Statistical Programmer to support clinical research and drug development programs. The role will be responsible for leading statistical programming activities across clinical studies, with a strong focus on the development, validation, and delivery of high-quality clinical trial datasets and statistical outputs.The Senior Statistical Programmer will work closely with Biostatistics, Clinical Data Management, Data Standards, Clinical Operations, and other functional teams to translate study requirements into efficient and compliant programming solutions. The individual will be expected to independently lead assigned programming activities, provide technical guidance to other programmers, and contribute to programming standards and process improvements.The successful candidate should have strong hands-on experience in SAS programming, CDISC standards, SDTM and ADaM, clinical trial reporting, and programming QC, along with a solid understanding of clinical trial methodology and regulatory expectations. Experience supporting oncology clinical trials and leading programming activities is highly preferred.Key ResponsibilitiesLead statistical programming activities for assigned clinical studies, projects, or programming deliverables.Review and interpret key study documents, including protocols, Statistical Analysis Plans (SAPs), CRFs, data specifications, metadata, and TLF mock shells.Develop, validate, and maintain SAS and/or R programs for clinical trial data processing and reporting.Develop and perform QC of SDTM and ADaM datasets, including complex derivations and associated documentation.Program, validate, and QC Tables, Listings, and Figures (TLFs) in accordance with study specifications and statistical analysis requirements.Develop and QC programming specifications for SDTM and ADaM datasets.Support the preparation, review, and QC of CDISC-compliant regulatory submission datasets and associated documentation.Develop and validate SAS programs for clinical trial-related activities, including randomization schedules where applicable.Write and maintain complex SAS macros and reusable programming utilities to improve efficiency, consistency, and quality.Review programming specifications, datasets, outputs, and QC results to identify discrepancies and ensure data and output integrity.Work closely with Biostatisticians, Clinical Data Managers, Data Standards teams, and other stakeholders to resolve programming and data-related issues.Provide programming input during study planning, analysis, and reporting activities.Take ownership of assigned deliverables and proactively identify potential risks, issues, and opportunities for process improvement.Participate in project and stakeholder meetings and provide technical input related to statistical programming and data standards.Support regulatory submissions, inspections, audits, and related documentation activities as required.Contribute to the development, enhancement, and implementation of programming standards, templates, macros, and best practices.Identify opportunities to improve programming processes, automation, efficiency, and quality across projects.Mentor and provide technical guidance to junior and mid-level statistical programmers.Conduct knowledge-sharing sessions and support the development of programming capabilities within the team.Perform additional project- or function-specific responsibilities as assigned.Quality & CompliancePerform all activities in accordance with Tech Observer's Standard Operating Procedures (SOPs), Quality Management System (QMS), applicable programming standards, and Good Clinical Practice (GCP).Maintain a strong focus on data integrity, traceability, reproducibility, and quality throughout the programming lifecycle.Ensure programming activities comply with applicable CDISC standards and regulatory expectations.Review and follow study-specific programming conventions, specifications, and documentation requirements.Support the preparation and implementation of SOPs, work instructions, standards, and process improvements where required.Maintain complete and accurate project documentation in accordance with established quality requirements.Required Qualifications & ExperienceBachelor's degree in Statistics, Biostatistics, Mathematics, Computer Science, Life Sciences, or a related discipline; Master's degree preferred.10+ years of relevant statistical programming experience within the pharmaceutical, biotechnology, CRO, or clinical research industry.Strong hands-on expertise in SAS programming.Experience with R programming is desirable.Strong knowledge of CDISC standards, including SDTM and ADaM.Demonstrated experience developing and QCing clinical trial datasets and Tables, Listings, and Figures (TLFs).Strong understanding of clinical trial methodology, protocols, SAPs, and clinical study data.Experience working with clinical study documentation such as CRFs, specifications, metadata, and mock shells.Experience with regulatory submission datasets and e-submission requirements.Experience developing complex SAS macros and reusable programming solutions.Experience with programming QC and validation processes.Hands-on experience supporting oncology clinical trials is strongly preferred.Demonstrated ability to independently lead programming deliverables or assigned projects.Experience mentoring junior programmers or providing technical leadership is preferred.Strong analytical, problem-solving, organizational, and decision-making skills.Excellent written and verbal communication skills with the ability to work effectively with cross-functional and client-facing teams.Preferred SkillsOncology clinical development experience.Experience supporting NDA/BLA/MAA or other regulatory submissions.Experience working with global clinical development programs.Knowledge of CDISC implementation and regulatory data standards.Experience developing programming standards, macros, templates, and automation solutions.Ability to work independently while effectively collaborating with geographically distributed teams.Demonstrated ability to learn new technologies, processes, and therapeutic-area requirements quickly.