Job Description

As Principal Biostatistician, adept at utilizing advanced statistical methods, you will support or lead multiple nonclinical projects. As part of this role, you will need to work in an unique scientific and collaborative environment for fast-moving projects, often under short timeline.

• Leading multiple nonclinical or manufacturing statistics supporting a big pharmaceutical company.
• Responsible for rigorous statistical methods to design experiments, analyze complex biological data, and support decision-making throughout the early drug development process
• Collaborate closely with scientists, toxicologists, regulatory experts, and data teams to ensure high-quality, interpretable results that accelerate the development of new medicines.
• Strong experiences in end‑to‑end statistical support across the product lifecycle, including analytical method development and validation, formulation development and robustness, and process development and validation—aligned with regulatory standards such as FDA, EMA, ICH, and USP
• Good oral and written skills are crucial, as is the desire to learn enough of the relevant science to interact effectively.
• Exposure to modern data analysis techniques and experiences such as data visualization, robust statistics and analysis of high-dimensional data is desirable.
• Constantly eager to expand statistical, communications, and computing expertise.
• Must enjoy the process of building long-term collaborative relationships, be at ease with either a non-rigid or rigid structure to projects and be organized in handling numerous projects simultaneously.

• PhD in Statistics or related field
• Solid knowledge of linear mixed models, multivariate analysis, design of experiments and Bayesian statistics is essential.
• Proficiency in the R language is required with the development of automated, reproducible workflows using R Markdown and Quarto to enhance regulatory submission readiness.
• Demonstrated written, oral, and personal communication skills.
• Ability to work autonomously and within a team environment.
• Demonstrated ability to work in interdisciplinary contexts outside statistics.