.About KueskiAt Kueski, we're dedicated to improving the financial lives of people in Mexico. Since 2012, we've been the leading buy now, pay later (BNPL) and online consumer credit platform in Latin America, known for our innovative financial services. Our flagship product, Kueski Pay, provides seamless payment solutions for both online and in-store transactions, establishing itself as the preferred option for a quarter of Mexico's top e-commerce merchants. Notably, we were the first to introduce BNPL on Amazon Mexico.We're a tech company with a culture geared toward innovation, collaboration, and impact, fostering a strong, diverse, and inclusive company culture. In 2023, Kueski was recognized as the top BNPL platform by Fintech Breakthrough and earned the title of one of Mexico's most ethical companies from AMITAI. Additionally, we ranked as one of the Best Companies for Female Talent by EFY.PurposeAnalysis of the behavior of the portfolio to define credit approval and fraud prevention strategies for the different credit products, such that business growth expectations are achieved, taking care of the level of losses, profitability, and optimal operating cost. Collaborate with multifunctional teams in the definition and implementation of analytical projects, infrastructure, and various control measures aligned with the established risk appetite.Key ResponsibilitiesConstruction of strategies for sustainable business growth and fraud prevention, through the development of monitoring systems and the definition of credit evaluation schemes, through an analytical and numerical perspective.Coordination of investigations for the massive identification of fraud patterns, derived from alerts, experiments, and control groups, through massive data analysis.Preparation of executive reports and presentations at a strategic level.Analysis of fraud in two levels: at portfolio to identify trends and suspicious behavior in customers (B2C) and at merchant/branch level identifying trends and patterns that may suggest fraudulent actions by store/merchant representatives (B2B). By developing methodologies for projecting credit losses associated with fraud, using data analysis methodologies, decision trees, clustering and other models.Quantitatively and qualitatively evaluate the need to incorporate additional information, new tools and/or new preventive policies to mitigate losses and prevent fraud.Coordinate and develop proofs of concept from external data sources to evaluate added value for fraud prevention and loss mitigation.A key point of contact with engineering and product to ensure appropriate development and implementation of fraud and risk policies and tools.Analysis of fraud and credit risk profiles to establish cross-selling strategies and campaigns to pre-approve credits in populations with good behavior to maximize the permanence and roots of clients with Kueski