Principal Biostatistician - Chemistry, Manufacturing and Control -

We are seeking a highly motivated Biostatistician to support CMC projects across the drug development lifecycle. The ideal candidate will have a strong foundation in statistical methodologies and programming and will play a critical role in analyzing complex manufacturing data to ensure product quality, consistency, and regulatory compliance. As a Principal Biostatistician, your responsibilities will include:

• Provide statistical support for CMC-related studies, including formulation development, stability studies, process validation, and analytical method validation.
• Collaborate with cross-functional teams, including analytical scientists, process engineers, and regulatory affairs, to develop and implement appropriate statistical strategies.
• Design and analyze experiments using linear mixed models, particularly for stability and variability analyses.
• Perform statistical modeling, data visualization, and simulations using R programming.
• Experience with Bayesian methods preferred.
• Ensure quality and compliance with internal standards and regulatory guidelines (e.g., FDA, EMA, ICH).
Here at Cytel we want our employees to succeed and we enable this success through consistent training, development and support. To be successful in this position you will have

A Biostatistician with in-depth Bayesian analysis experience and a strong interest in Chemistry, Manufacturing, and Controls (CMC) data analysis. We welcome recent PhD graduates to also apply for this position. You are excited and enthusiastic, motivate your teams to do great work and collaborate easily with your clients. You never settle for what is, but always push clinical development forward to what it could be. You motivate others to do the same.,
• PhD in Biostatistics, Statistics, or a related quantitative discipline.
• A strong interest in supporting CMC or related manufacturing/analytical projects in a pharmaceutical or biotechnology setting.
• Strong proficiency in R programming for statistical modeling, analysis, and visualization.
• Solid understanding and practical application of linear mixed models .
• Excellent communication and collaborative skills to work effectively across teams.
• Exposure to or hands-on experience with Bayesian statistics and its application in industrial settings., Familiarity with a wide variety of statistical transformations and distributions, appropriate for CMC data.