**Description:** Academic background in Computer Science, Statistics, Mathematics, Engineering, or related fields; Proficiency in data analysis tools and technologies such as Python, R, SQL, and others; Data-driven analytical culture; Experience in fraud prevention and authentication strategies and policies. Strong analytical and problem-solving skills, with the ability to translate data into actionable recommendations for strategic decision-making. Development and implementation of fraud prevention and customer authentication policies for credit product underwriting, aiming to reduce losses and improve process efficiency; Testing of new technologies and information sources for fraud prevention and authentication, including impact analysis on performance and costs; Optimization of the credit proposal decision pipeline, focusing on process optimization and automation; Monitoring and control of fraud, complaint, civil litigation, and loss indicators, as well as analysis team decisions, to identify improvement opportunities. #LI-HYBRID #LI-LA1 251217020255428305