Must possess an advanced degree – PhD in Biostatistics, Statistics, or a closely related quantitative discipline (or an MD with strong biostatistical training)
Proven mastery of fundamental concepts in clinical biostatistics, as well as being up-to-date on latest ideas and publications, including: Deep understanding of developing statistical analysis plans (SAPs) in pharma; Experience of clinical trial simulation; Adaptive clinical trials; Bayesian network meta-analysis; Platform clinical trials
Deep understanding of the data sources, AI methods and related analytical technologies, e.g., AI principles, deep learning, machine learning (supervised or unsupervised), causal inference, large language models, foundation models, diffusion models, reinforcement learning, and knowledge graphs
Ability to develop new-to-world solution architecture blueprints, and decompose the end-state solution into incremental releases to prove feasibility and deliver value
Track record of innovation at the intersection of AI and clinical trial design / biostatistics
Track record of recent leadership and engagement in the external ecosystem via publications, conference participation, keynote speeches, and connectedness with academia and/or industry
Experience managing direct reports, or a complex network of internal & external stakeholders
Compelling communicator with track record of translating technical methods to non-technical executive stakeholders
