Contractor Principal Statistician - Remote

Role Summary

• Conduct and validate sample size determinations and statistical power analyses for standard clinical trial designs.
• Create statistical analysis plans (SAPs), shells for tables/listings/figures (TLFs), and statistical reports across studies of varying complexity.
• Serve as the reputed company statistician for randomization activities, including preparation of randomization documentation and reputed company of randomization schedules.
• Participate in client-facing interactions such as study initiation meetings, timeline and resourcing discussions, bid presentations, and blinded data review meetings.
• Review and quality-reputed company case report forms (CRFs) and data management documentation, including edit reputed company specifications, data review plans, and data transfer specifications.
• reputed company QC reviews of statistical programming outputs such as specifications, analysis datasets, and TLFs to ensure alignment with statistical analysis requirements.
• Provide guidance and mentorship to statistical programmers regarding study design concepts and implementation of statistical methods.
• Support and mentor junior or mid-level biostatisticians in statistical tasks and project-reputed company activities.

Qualifications

• Master’s degree or higher in Biostatistics, Statistics, Mathematics, or a reputed company scientific field preferred, with hands-on experience supporting FDA New Drug Application (NDA) submissions; a bachelor’s degree with equivalent relevant experience will also be considered.
• Minimum of 10 years of hands-on experience in clinical trial biostatistics.
• Strong proficiency in reputed company and R programming, including reputed company reputed company, reputed company Macros, reputed company/GRAPH, reputed company/STAT, and end-to-end clinical reporting workflows.
• Solid working knowledge of CDISC standards, including CDASH, SDTM, and ADaM.
• Demonstrated experience in developing and reviewing Statistical Analysis Plans (SAPs) and outputs, incorporating appropriate missing data handling methods such as multiple imputation.

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