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.
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 Senior Advisor of Statistics in the Cardiometabolic Health (CMH) at Eli Lilly, where he leads statistics for early-phase cardio-renal programs. Since joining Lilly in 2020, Dr. Wang has contributed to the development of therapies for heart failure, chronic kidney disease, and atherosclerotic cardiovascular disease, with responsibilities spanning study design, data analysis, and data-driven decision making. He has also collaborated with clinicians and academic partners on research involving predictive biomarkers for heart-failure outcomes, phase 2 to 3 decision-making, and functional data analysis for high-frequency biosensor data. Prior to joining Lilly, Dr. Wang was a Research Assistant at the Center for Survey Statistics and Methodology at Iowa State University, where he supported large-scale survey projects—including the U.S. Pet Ownership and Demographics Survey for the AVMA—and provided statistical consulting across veterinary medicine, ecology, and education. Dr. Wang earned his Ph.D. in Statistics from Iowa State University in 2020, with a dissertation focused on high-dimensional time-series analysis and economic forecasting. His academic and professional journey reflects a strong commitment to developing and applying innovative statistical methods to address meaningful real-world challenges 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
Event Type
- NISS Hosted