Revenue Management - Sales BrazilContext: No available data, newly formed team, and limited information about the systems.Actions:- Acquisition of market research data and application of intelligence for prioritizing items in the Marketplace;- Big data management of promotions to rank by highest customer interest in the app, using Azure DataBricks, Data Factory, Airflow, Git, Datadog, and Azure DevOps;- Application of intraday weather data for over 5,000 Brazilian cities through Python, using a solution provided by IBM, for models and dashboards;- Optimization of over 200K monthly promotions, selecting the product, its volume, and the percentage discount offered, with the choice of processing resources, RabbitMQ and Service Bus communication, and storage;- Integration with the global Bees and Ab-Inbev platform through Unity Catalog.Results:- 70% reduction in cloud process execution costs in partnership with Databricks;- Direct influence on a 10% increase in the company's EBITDA;- Contribution to a 10-point increase in internal and external NPS.

Anti-FraudContext: Fraud detection relying solely on a locally installed SQL server.Actions:- Participated in the implementation of Santander's Data Warehouse;- Optimized the use of Git and the Cloudera Workbench platform;-ETL of data from various sources (MS Access, MS SQL Server, CSV, Data Lake, Excel, and API) for real-time fraud detection in all bank transactions worldwide (> 1 billion);- Structured tables for MIS in streaming;- Created dashboards in QlikView, Tableau, and Excel;- Developed the Civil Actions Anti-Fraud system, with integration to Correios (Brazilian postal service) for notifications to customers involved in fraud, autonomous contract termination, distribution of documents to law firms, and recovery through legal means.- Strategy analyst for Lynx Real-Time Fraud Detection;- Developed the Python library for AntiFraud;- Developed the system for detecting and acting on registration and transactional anomalies.Results:-Achieved an 80% reduction in fraud value (approximately U$200M).

Context: Monthly planning conducted via Excel spreadsheets using averages.Actions:- Optimization of operational planning by using analytical data in SQL SAS, based on each customer's options in the REDE acquirer's voice, email, and chat service centers, directing each customer to the most suitable operator according to their skills.- Unification of service centers and adjustment of service hours based on usage data.Results:- Savings of US$5M/year by reallocating support resources to address the most common customer inquiries.- Improved efficiency in the availability of service centers, resulting in savings of U$10M/year.

Operational Planning (Forecast, Capacity, Indicators, Big Data, and Management) of high-standard card service centers for the Card Directorate.

Responsible for Routing and Capacity Control of service centers through the Genesys solution in real-time, production of indicators using SQL and Access databases, development and execution of contingency plans in collaboration with suppliers and internal clients.

- Maintenance of operating systems;- Replacement and repair of PC and laboratory hardware;- Installation and structuring of data networks across the campus;- Security of internal data systems.