• Led the end-to-end architecture of Data Science and AI solutions on AWS, leveraging S3 and integrating MLOps workflows to automate training, validation, deployment, and continuous model monitoring. Spearheaded data governance initiatives under a Data Mesh approach, promoting responsible decentralization of data domains and adopting a medallion architecture to ensure scalability, traceability, and data quality from ingestion through analytical consumption. • Developed and implemented an advanced fraud detection model for the Claims team, combining gradient-boosting algorithms, statistical analysis, and Explainable AI. This solution reduced fraud losses by 13% and case analysis time by 28%. • Designed and deployed an intelligent chatbot powered by GPT-4 via secure APIs, integrating CRM and policy-system data to provide the sales team with instant access to metrics, quotations, and comparisons. As a result, commercial productivity increased by 30%, and decision-making was significantly accelerated.

• Optimized collections strategies through Machine Learning models aligned with key financial KPIs, enhancing operational efficiency and recovery outcomes. • Predicted transitions to non-collectible delinquency using a churn prediction model, significantly improving portfolio recovery rates. • Replaced the precredit system with an in-house behavioral prequalification model, eliminating external licensing costs and increasing control over decision criteria.

• Developed a route optimization model for 5B trucks, dynamically adjusting routes based on historical patterns to reduce operational costs and enhance safety.

• Supervised teams of up to 35 people, managing operational planning, data analysis, and visualizations to support regional decision-making.