Principal Biostatistician

Role overview

Provide senior statistical leadership across clinical development studies, from protocol design and analysis planning through reporting and regulatory support. You’ll advise study teams and sponsors, lead statistical activities to agreed timelines, and oversee the quality and delivery of statistical programming and analyses. The work includes complex study designs and may involve unblinded analyses for data monitoring committees.

Responsibilities - Develop and review protocol methods, randomization plans, sample size calculations, and statistical analysis plans, including complex designs. - Generate or oversee analysis datasets, tables, figures, listings, and SAS programs for clinical trial data. - Review statistical outputs and study documents for accuracy, consistency, and quality. - Provide statistical results and reports to study teams, sponsors, and data monitoring committees; review statistical sections of clinical study reports. - Consult with clients and collaborators on study design, supplemental analyses, and regulatory or other requests. - Lead study statistics within budget and schedule; review project financial health, scope, invoices, and the work of other biostatisticians.

Requirements - Master’s degree or equivalent in statistics, biostatistics, or a related field plus at least 10 years of experience; or a PhD in one of these fields plus at least 7 years. - Experience with SAS and strong statistical programming skills. - Broad, advanced knowledge of statistical methods and the principles underlying clinical trials. - Familiarity with relevant clinical research standards and regulations, including ICH guidance, CDISC data structures, 21 CFR Part 11, and FDA guidance. - Strong consulting, communication, presentation, organization, and problem-solving skills; able to coordinate work and meet deadlines. - Sound judgment and the ability to build effective working relationships and guide others.

Nice to have Experience in oncology or rare diseases, late-phase studies or submissions, client consulting on study design, or specialized methods such as adaptive designs and missing-data approaches. Familiarity with statistical software beyond SAS is also preferred.