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Contractor Principal Statistician - Remote

Work from home Full-time role Hiring

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 lead statistician for randomization activities, including preparation of randomization documentation and generation 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-check case report forms (CRFs) and data management documentation, including edit check specifications, data review plans, and data transfer specifications.
  • Perform 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-related activities.

Qualifications

  • Master’s degree or higher in Biostatistics, Statistics, Mathematics, or a related 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 SAS and R programming, including Base SAS, SAS Macros, SAS/GRAPH, SAS/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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