Sr. Biostatistician

Responsibilities:

• Provide statistical input for protocol development, study design, and CRF development, actively contributing to internal and client meetings, and discussing sample size scenarios.
• Conduct sample size calculations and draft statistical methodology sections for study protocols.
• Oversee the production and quality control of randomization schedules, statistical analysis plans, reports, and other supporting documents.
• Design statistical analysis plans and table shells for study protocols or integrated summaries for NDA submissions.
• Develop SAS programs for generating and quality-checking statistical tables, figures, and listings.
• Perform mapping from raw datasets to CDISC SDTM datasets.
• Produce and quality control define.pdf, define.xml, and annotated case report forms for SDTM submission.
• Develop SAS programs to generate and validate analysis datasets in CDISC ADaM or sponsor-specified formats.
• Review and contribute to the statistical and results sections of clinical study reports and publications.
• Review case report forms and data validation guidelines to ensure data integrity.
• Provide statistical input for Data Monitoring Committee activities and serve as an independent statistician supporting DMC review.
• Understand regulatory requirements and their implications for statistical methodology and analysis.
• Support proposal development and participate in bid defense meetings.
• Review Request for Proposal and Scope of Work documents, ensuring alignment with project goals.
• Manage project budget and forecast resources needed for studies.

Education:

• Master’s degree in Biostatistics, Mathematics, Statistics, Public Health, or related field required.
• PhD in Biostatistics, Mathematics, Statistics, Public Health, or related field preferred.

Experience:

• Minimum of 10 years of experience in statistical analysis of clinical trials data, with a minimum of 3 years within a CRO.

Skills:

• Proficiency in SAS statistical programming.
• Familiarity with general linear models, mixed models, survival analysis, categorical data analysis, and non-parametric methods.
• Knowledge of other statistical and data management software packages is advantageous.
• Strong written and oral communication skills.
• Proficiency in CDISC data standards and models.
• Strong project management and leadership skills.
• Thorough understanding of clinical research regulatory requirements, such as GCP and ICH guidelines.
• Ability to manage multiple tasks and projects effectively.
• Capacity to articulate statistical techniques clearly and interpret results accurately.