Principal Biostatistician; Associate Director Level

About the Role

This role is in Early Development Biometrics (EDB), a core function within Product Development Data Sciences (PDD) that provides strategic leadership and scientific rigor across early clinical development  partner across Biostatistics, Analytical Data Science, and Data Management to enable data‑driven decision‐making from first‑in‑human through proof‑of‑concept studies. As trusted partners in early development, we design efficient and innovative clinical trials, apply rigorous statistical methods, and implement high‑quality programming and analytical solutions to accelerate timelines, de‑risk development, and increase the probability of technical success.

• Lead the statistical design of complex or high priority early phase studies, including adaptive, platform, and biomarker enriched trials.
• Champion the use of innovative methodologies, for example Bayesian designs and model‑based dose selection, aligned with exploratory objectives and scientific hypotheses.
• Ensure strategic alignment and quality across statistical deliverables within early phase programs, shaping the statistical framework that supports phase transitions, internal governance reviews, and regulatory interactions.
• Solve complex statistical challenges in early development using deep expertise and novel approaches, such as integrating translational data, navigating sparse or emerging datasets, and balancing speed with scientific rigor.
• Interpret and drive the narrative around emerging data, translating early signals into clear, meaningful insights for internal stakeholders and external communications.
• Own the statistical content of major early regulatory submissions, such as INDs, EMA scientific advice packages, and pre‑IND or end‑of‑Phase 1 meetings, ensuring clarity, integrity, and scientific robustness.
• Lead substreams and functional contributions within disease area strategy and the integration of novel data sources, for example omics, imaging, and real‑world data.
• Maintain strong connectivity and collaboration with other core biometrics functions within PDD, including Biostatistics, Analytical Data Science, and Data Management, ensuring early phase analysis strategies are executable.

• PhD or MSc in Statistics, Biostatistics, or a closely related quantitative field.
• At least 6 years of experience in clinical trial statistics, including responsibility for study‑level strategy and decision making.
• Proven ability to lead statistical input into clinical development plans and regulatory submissions.
• Strong expertise applying advanced statistical methods to enable data‑driven decision making.
• Proficiency in SAS and/or R and deep familiarity with CDISC standards and evolving regulatory data requirements.
• Independent thinking and sound decision‑making principles.

• Extensive experience working in cross‑functional, global early development teams, with demonstrated ability to collaborate effectively across Clinical Development, Clinical Pharmacology, Translational Medicine, Regulatory, and Biomarker teams.
• Effective communication skills, including the ability to translate complex or exploratory statistical methodologies into actionable insights for clinical, regulatory, and non‑technical audiences.
• Experience across multiple phases of drug development, with a strong emphasis on early‑phase (Phase I/II) trials, including first‑in‑human, dose‑finding, and proof‑of‑concept studies.
• Excellent communication and influencing skills, with a proven track record of shaping clinical strategy, informing go/no‑go decisions, and aligning statistical strategy with program goals.
• Demonstrated scientific leadership and mentoring within a matrixed organizational structure, guiding junior statisticians and cross‑functional colleagues on the application of innovative design and analysis techniques.
• Strategic mindset, with the ability to assess risk, anticipate data needs, and contribute to portfolio‑level decisions, including dose/regimen optimization and early investment prioritization.
• Familiarity with innovative methods (e.g., Bayesian adaptive design, MIDD, complex modeling) and a working knowledge of evolving…