● Designed and implemented an end-to-end statistical predictive and machine learning scoring model for credit candidates, increasing product acquisition by 15% while strictly optimizing portfolio quality. ● Conducted regular calibration, validation, and monitoring of predictive algorithms to maintain robust data accuracy, reliability, and close alignment with core business objectives. ● Utilized Python to build automated data pipelines, perform anomaly detection, clean complex datasets, and streamline analytical processing. ● Engineered relational database queries, transformations, and advanced data extractions leveraging SQL Server. ● Developed comprehensive interactive BI dashboards using Power BI and advanced DAX to communicate performance metrics and predictive insights to stakeholders.

● Supported the validation and preparation of high-volume datasets for credit risk admission models using SAS Enterprise and SAS Miner. ● Audited data consistency across internal relational systems, documenting validation processes and generating quality reports.

● Developed quantitative inventory forecasts based on historical storefront consumption, optimizing supply availability and minimizing shortages. ● Automated data tracking and reporting procedures through internal Excel macros, increasing processing efficiency.