Rafael is a data scientist specialized in advanced analytics, machine learning and scalable data infrastructure. With a background in electromechanical engineering, he brings a strong quantitative and technical foundation to solving complex challenges in the energy and corporate sectors.

His trajectory spans data science, research and development, and analytics roles across the energy sector and corporate environments in the Dominican Republic. Throughout his career, he has worked on machine learning applications, sales forecasting, time series modeling, spatial data visualization and data pipeline automation aimed at optimizing performance and driving strategic decisions.

He has experience in cloud and big data environments like Databricks and PySpark, where he has contributed to demand planning, clustering models and data-driven solutions that help organizations anticipate outcomes and improve operational efficiency. His work combines technical depth with the ability to translate complex analytical outputs into practical business insights.

Rafael also brings a robust academic foundation in data science, applied mathematics and electromechanical engineering. He holds a Master of Science in Data Science from Instituto Tecnológico de Santo Domingo (INTEC) and a Bachelor of Science in Electromechanical Engineering from Universidad Autónoma de Santo Domingo (UASD).

At oPHtimize, Rafael focuses on the design and architecture of robust data infrastructure. He leads the engineering efforts required to build scalable data pipelines and high-performance analytical environments, ensuring the underlying technical framework is optimized to support complex machine learning models and data-driven decision-making.