Manuel is a data leader specialized in advanced analytics, machine learning, and scalable data infrastructure. With a background in data science, applied mathematics and economics, he brings a strong quantitative foundation to solving business problems and supporting decision-making through data.

His trajectory spans data science, research and development, and analytics roles across the energy, finance, education, and technology-related sectors in the Dominican Republic. Throughout his career, he has worked on mathematical modeling, forecasting, data visualization, and machine learning applications aimed at improving performance, strengthening analysis, and generating actionable insight.

He has particular experience in Azure-based analytics and machine learning environments, where he has contributed to the design of scalable data pipelines, advanced models, and data solutions that help organizations better understand performance, anticipate outcomes, and improve execution. His work combines technical depth with the ability to translate complex analytical outputs into practical business insight.

Manuel also brings a strong academic foundation in applied mathematics, statistics, machine learning, and economics. He holds a Master’s degree in Applied Mathematics from INTEC, where his thesis focused on the dynamic analysis of human brain signal behavior. He also completed postgraduate studies through MITx in Statistics and Data Science and earned his undergraduate degree in Economics from INTEC, graduating Magna Cum Laude.

At oPHtimize, Manuel oversees the data infrastructure and leads the development of advanced analytics models that support insight generation and decision-making.