Opportunities and Careers for Biostatisticians in Today’s ‘AI World’

Statistics is often viewed as a narrow discipline focused on p-values and confidence intervals, but in reality, it is the broad science of collecting, analyzing, and interpreting data by quantifying variability and bias, which is an essential foundation in today’s AI-driven world. This session begins by challenging common misconceptions and highlighting the growing importance of biostatistics in health and data science. Building on this perspective, Shamshad Ali, Associate Director, BCCR-DC, will demonstrate how biostatisticians and data managers function as peer scientists, working collaboratively to promote rigor, transparency, and reproducibility across the research lifecycle. Through real-world examples, the webinar will also highlight the BCCR-DC model, grounded in trust, accountability, communication, collaboration, and leadership, while showcasing career pathways and opportunities in the field.

About Douglas Landsittel and Shamshad Ali
Dr. Douglas Landsittel serves as Professor and Chair of Biostatistics and Director of the Biostatistics Center for Collaborative Research and Data Coordination. His research focuses on biomarkers and prognostic models, and comparative effectiveness and causal inference. He has published over 180 papers and is a Fellow of the American Statistical Association. He was the PI of a PCORI Methods contract and an AHRQ R25 research education program and has served as on many expert panels and study sections, including Chair of the Safety and Occupational Health Study Section. 

Shamshad Ali is Associate Director of the Biostatistics Center for Collaborative Research and Data Coordination at the University at Buffalo. He holds master’s degrees in Applied Mathematics and Statistics and has over two decades of experience in clinical research. His expertise spans study design, randomization, data management, statistical analysis, reporting, regulatory submissions (FDA,EU) and scientific communication. Mr. Ali is passionate about mentoring students and peers while advancing statistically robust, transparent, and reproducible research across collaborations.