Senior/Principal Biostatistician Contractor - Remote

Datawise Inc; Boston (MA); On-site; USD 120,000 - 180,000; Full time

Job summary

Datawise Inc. is seeking a senior biostatistician to lead sample size determinations and power analyses for diverse clinical trial designs, and to craft SAPs, TLF shells, and statistical reports.

You will serve as the lead statistician for randomization activities, preparing documentation and schedules, while guiding junior statisticians and collaborating with clients. The role requires 5+ years in clinical trial biostatistics, strong SAS and R skills, and CDISC standards knowledge.

Qualifications

• Hands-on experience supporting FDA NDA submissions.
• 5+ years of hands-on experience in clinical trial biostatistics.
• Oncology trial experience preferred.
• Strong proficiency in SAS and R programming.
• Solid working knowledge of CDISC standards (CDASH, SDTM, ADaM).
• Experience in developing and reviewing SAPs and outputs.

Responsibilities

• Conduct and validate sample size determinations and power analyses for standard clinical trial designs.
• Create SAPs, shells for tables/listings/figures (TLFs), and statistical reports across studies of varying complexity.
• 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 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.

Skills

SAS

R programming

CDISC standards

SAP development

Clinical trials

Education

Biostatistics degree

Job description

• 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

• BS/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.
• 5+ years of hands‑on experience in clinical trial biostatistics. Oncology trial working experience is preferred.
• 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.