Selected Work & Community Impact
Expert data science consulting delivering machine learning solutions, predictive modeling, and advanced analytics across healthcare, research, and data-driven organizations.
Comprehensive solutions tailored to your analytical needs, from predictive modeling to custom tool development
Custom ML solutions using ensemble methods, neural networks, and advanced algorithms. Specializing in classification, regression, and time-series forecasting with comprehensive model evaluation.
In-depth statistical analysis and visualization to uncover patterns, relationships, and insights. Author of production-grade EDA toolkits used across research institutions.
Develop sophisticated models for healthcare outcomes including kidney disease risk estimation, ICU mortality prediction, and patient stratification tools validated against clinical standards.
Build proprietary Python libraries and analytical frameworks. Experience creating model diagnostics tools, SHAP explainers, and automated evaluation pipelines for reproducible research.
Corporate workshops and academic instruction in data science fundamentals, machine learning techniques, and best practices. Curriculum development for university-level courses.
Partner on academic research projects with emphasis on healthcare analytics, clinical decision support systems, and methodological innovation. Co-authorship and publication support.
Data Science Dynamics is founded by Leon Shpaner and Oscar Gil, bringing together decades of combined expertise in healthcare analytics, financial analysis, machine learning, and clinical research.
With a track record at a top academic medical center and teaching appointments across five universities, our team specializes in translating complex data challenges into practical, validated solutions.
We combine rigorous statistical methodology with cutting-edge machine learning techniques to deliver results that meet both academic standards and real-world operational needs.
(publishes academically as Leonid Shpaner) is a Data Scientist at UCLA Health. With over 15 years of experience in analytics and teaching, he has worked across healthcare, financial services, education, and personal development. He serves as an adjunct professor at various institutions, including the University of San Diego, where he teaches statistics and machine learning using Python in the M.S. in Applied Artificial Intelligence program.
is a Data Scientist at the University of California, Riverside, bringing over ten years of professional experience in the education data management industry. An effective data professional, he excels in Data Warehousing, Data Analytics, Data Wrangling, Machine Learning, SQL, Python, R, Data Automation, and Report Authoring. Oscar holds a Master of Science in Applied Data Science from the University of San Diego.
is a Professor of Practice and Academic Director of the Applied Data Science (MS-ADS) and Applied Artificial Intelligence (MS-AAI) programs at the University of San Diego's Shiley-Marcos School of Engineering. A former senior technical advisor for the CDC's National Institute for Occupational Safety and Health (NIOSH) and visiting scholar at UCSD's San Diego Supercomputer Center, he holds a PhD in Geo-Engineering from the University of Nevada, Reno, an MSc in Data Science from Michigan Tech, and an MBA from UNR. He has published widely in international journals and received numerous university, national, and international awards.
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