ASA Connect

Event Page: NISS Virtual Career Panel: Statistical Careers in BioPharma | National Institute of Statistical Sciences

Date: Friday, November 14, 2025 - 12:00pm to 1:00pm ET

Zoom Registration Link: https://us02web.zoom.us/webinar/register/WN_dF-Ui5sAQyWFAcBmrQxHnA

Join the National Institute of Statistical Sciences for a virtual career panel focused on career pathways in the biopharmaceutical industry. This session will highlight how statisticians contribute to drug development, clinical trials, data science innovation, and decision-making across the BioPharma landscape.

Panelists representing leading organizations will share insights into their career journeys, typical responsibilities in their roles, and the skills and experiences that are most valuable for success in this sector. Participants will gain a practical understanding of how statistical training translates into impactful work that advances healthcare and patient outcomes.

This event is designed for students, early-career professionals, and anyone interested in learning more about applying statistics and data science in BioPharma. A live Q&A will follow the panel discussion, providing attendees the opportunity to engage directly with the speakers.

Register on Zoom Here!

Panelists:
Dr. Zhenzhong Wang, Eli Lilly

Dr. Oluyemi Oyeniran, Johnson & Johnson

Dr. Kaihua Ding, AstraZeneca

Moderator:
Dr. Richard Baumgartner, Merck and Co., Inc.

About the Panelists:

Dr. Zhenzhong Wang is a Research Scientist in Statistics at Eli Lilly and Company, where he applies advanced statistical methodologies to support data-driven decision-making in pharmaceutical research and development. Since joining Eli Lilly in 2020, Dr. Wang has contributed to the design, analysis, and interpretation of complex datasets, leveraging his expertise in spatio-temporal modeling, high-dimensional time series analysis, Bayesian inference, and macroeconomic modeling and forecasting. Before joining Eli Lilly, Dr. Wang served as a Research Assistant at the Center for Survey Statistics and Methodology at Iowa State University. There, he worked on large-scale survey projects, including the Survey of U.S. Pet Ownership and Demographics for the American Veterinary Medical Association, where he was involved in sampling design, missing data imputation, data aggregation, and post-stratification estimation. Dr. Wang earned his Ph.D. in Statistics from Iowa State University in 2020. His academic and professional trajectory reflects a deep commitment to developing and applying innovative statistical methods to solve real-world problems, particularly those at the intersection of data science, economics, and public health. See Profile

Dr. Oluyemi Oyeniran, Ph.D., is a Senior Principal Scientist in the Statistics and Decision Sciences Department at Johnson and Johnson Innovative Medicine. He specializes in providing advanced statistical expertise across the biologics development and manufacturing lifecycle, including CMC regulatory submissions, analytical method development, manufacturing and formulation, and continuous manufacturing. His technical proficiencies include experimental design, linear and non linear mixed effects modeling, Bayesian methods, machine learning applications, and statistical approaches for continuous manufacturing. He holds a PhD in Statistics from Bowling Green State University. See Profile

Dr. Kaihua Ding is the Director of Data Science and Artificial Intelligence at AstraZeneca, where he leads strategic initiatives at the intersection of statistical evaluation, causal modeling, and adjoint-based optimization within the AZ Brain Group. His work focuses on developing variance-bounded evaluation frameworks and scalable AI methodologies to support decision-making across drug discovery and clinical development, ensuring models not only perform but perform reliably under real-world uncertainty. Before joining AstraZeneca, Dr. Ding served as Machine Learning Engineering Manager at United Airlines, where he guided the deployment of production-scale machine learning systems to optimize aviation operations and customer experience. Prior to that, he was a Staff Computational Scientist at the University of Chicago, contributing to high-performance computing (HPC) research and interdisciplinary modeling collaborations. Earlier in his career, he spent several years as an R&D Software Engineer at ANSYS, where he worked on high-order numerical methods, algorithm design, and error estimation for large-scale simulation platforms. Dr. Ding earned his PhD in Engineering from the University of Michigan, where his research integrated advanced numerical analysis, HPC, and model accuracy assessment techniques. His expertise reflects a sustained commitment to bridging theory and practice-building AI systems that are mathematically grounded, computationally efficient, and impactful in real-world deployment. Across academia, industry, and biopharmaceutical innovation, Dr. Ding exemplifies a leader advancing the next generation of trustworthy, interpretable, and optimally designed AI systems. See Profile

About the Moderator:

Dr. Richard Baumgartner is a Senior Director with the Biometrics Research Department, Biostatistics and Research Decision Sciences (BARDS) at Merck and Co., Inc. in Rahway, NJ. During his time at Merck, he has supported early clinical and preclinical studies with imaging components, including functional Magnetic Resonance Imaging (fMRI), dynamic contrast-enhanced MRI (DCE-MRI), and Positron Emission Tomography (PET) imaging, in the fields of neuroscience, inflammation, and cardiovascular therapeutics. He is currently involved also in several projects in the field of Artificial Intelligence and Machine Learning (AIML). Previously, he held the position of Associate Research Officer at the Institute for Biodiagnostics, National Research Council Canada in Winnipeg, Canada, where he worked on the development of methods for exploratory analysis of fMRI. At the Institute for Biodiagnostics, he also worked on metabolomic applications to develop diagnostic biomarkers for the prediction of pathogenic fungi and breast cancer. Richard holds a PhD in Electrical Engineering from the University of Technology Vienna, Austria. See Profile