Bernardo Dias Raimundo holds a master's degree in Statistics and Information Management with a specialization in Risk Analysis and Management from Nova IMS and a bachelor’s degree in economics from ISEG-UTL. Professionally, he brings a wealth of experience in internal audit, consulting, and risk management roles. His expertise spans across consulting firms, nonprofit institutions, and financial institutions, where he has effectively applied his skills to navigate and address complex challenges within diverse organizational contexts. Furthermore, Bernardo's professional journey extends into the dynamic domain of machine learning. In this arena, he leverages his academic specialization to harness the power of statistical and information management techniques. During his Nova IMS master’s program, he focused his thesis on the application of machine learning model combinations in credit scoring methods. His research efforts culminated in the publication of a scientific paper, showcasing his contributions to advancing methodologies in the field. His scientific interest focusses on Data Science for Finance specifically in portfolio optimization, risk management and credit scoring.