Partner, Chief Methodologist & Statistician
Igor provides strategic leadership in research design, measurement, and advanced analytics. With a career that spans both academia and applied industry settings, Dr. Himelfarb specializes in developing rigorous methodological frameworks that transform complex data into reliable, decision-ready insights. His work focuses on ensuring analytic precision, measurement validity, and the clear translation of technical findings into actionable intelligence for organizational leaders.
In addition to his role at NovaMetrica, Dr. Himelfarb serves as Associate Professor of Business Administration at Lincoln University (California), where he teaches graduate and doctoral courses in Statistics, Econometrics, Machine Learning, Quantitative Methods for Business and Finance, and Business Research Methodology. His academic and applied work reflects a deep commitment to bridging theoretical rigor with real-world problem solving—particularly in high-stakes environments where data quality, interpretation, and inference are critical.
Dr. Himelfarb holds a Ph.D. in Education with an emphasis in Research Methodology and Quantitative Methods in the Social Sciences from the University of California, Santa Barbara, along with an M.A. in Statistics from UCSB and an M.B.A. in Marketing and Data Analytics from Colorado State University. He also earned an M.A. in Educational Psychology and a B.A. in Psychology from California State University, Northridge. Prior to his academic appointments, he held research and psychometric roles at Gallup and the Educational Testing Service (ETS), where he contributed to large-scale measurement and analytics initiatives. An active scholar and reviewer, Dr. Himelfarb has an extensive publication record and continues to advance the fields of psychometrics, applied statistics, and data-informed decision science.
Partner, Chief Technology Officer
Maxim leads the design and implementation of scalable technology solutions that power advanced analytics and data-driven innovation. With over two decades of experience in full-stack software development, systems architecture, and digital transformation, Uvarov specializes in building robust data infrastructures that enable organizations to turn complex information into operational intelligence and strategic advantage.
Maxim holds a Master of Science in Computer Science and has completed specialized training in relational databases, data analysis, and cloud platforms. Throughout his career, he has led the development of enterprise-grade analytics environments, including the design of ETL pipelines for large-scale data integration, interactive dashboard ecosystems for executive decision-making, and big-data
reporting frameworks leveraging modern cloud and distributed computing technologies. His technical leadership has supported mission-critical initiatives for organizations ranging from federal agencies to global information services firms.
Prior to joining NovaMetrica, Maxim served as CEO of Maximize Solutions and held senior engineering roles at ProQuest and Philadelphia Shipyard, where he led the development of complex digital platforms and logistics systems. He has a proven track record of modernizing legacy environments, managing cross-functional and offshore teams, and implementing analytics architectures across multi-site digital ecosystems. At NovaMetrica, Maxim bridges technology and analytics to ensure that sophisticated data solutions are not only technically sound but also strategically aligned with client objectives.
Director of Data Sciences
Guoliang leads the design and implementation of advanced analytical solutions that transform complex data into meaningful, decision-ready insights. With doctoral training in Applied Mathematics at Penn State, an M.A. in Educational Measurement from the University of Illinois Chicago, and a B.S. in Applied Mathematics from Beijing Normal University, Guoliang brings a strong theoretical and methodological foundation to his work. His academic background enables him to approach data not simply as
information, but as structured evidence capable of revealing patterns, relationships, and predictive signals that drive strategic understanding.
Across both the education and mortgage sectors, Fang has built a track record of tackling large-scale, complex data challenges. He specializes in integrating diverse data sources, developing robust analytical models, and extracting actionable intelligence from complex and often imperfect datasets. His work has focused on uncovering hidden trends, optimizing decision processes, and designing data frameworks that allow organizations to move beyond descriptive reporting toward deeper diagnostic and predictive insight.
