Working on Customer Segmentation Analysis and Market. Spearheaded the development of a recommendation system that, in conjunction with customer segmentation, improves GMV through assertive customer communication★ Key Achievements ★⇨ Implementation client profiling using statistical analysis and machine learning models that resulted in a 6x increase in sales with the same budget spend.

Development and implementation of data science, machine learning, and data engineering solutions to support infrastructure.★ Key Achievements ★⇨ Architecture for connecting the MainFrame in Cobol to an ETL using NiFi flow, Flink, and Grafana for real-time data monitoring, which previously took at least 8 to 16 hours to update.⇨ Time series model using Markov chains (and its respective regression technique) for identifying errors in the data server related to the payment system… Show more Development and implementation of data science, machine learning, and data engineering solutions to support infrastructure.★ Key Achievements ★⇨ Architecture for connecting the MainFrame in Cobol to an ETL using NiFi flow, Flink, and Grafana for real-time data monitoring, which previously took at least 8 to 16 hours to update.⇨ Time series model using Markov chains (and its respective regression technique) for identifying errors in the data server related to the payment system and their occurrence, reducing up to 90% of the delay to report problems to on-call engineers.⇨ Modelling and Deplyment of the full project end-to-end of the fraud detection for the product credit card, reducing false negative rates by 40% compared to the previous system. Show less

Development and implementation of data science and machine learning solutions for business decision-making, primarily related to product opportunity and financial health, using cloud services such as AWS, Azure, and Databricks for robust and reliable implementation.★ Key Achievements ★⇨ Designed a forecasting model to predict revenue using the Dora Framework and AWS for deployment. The new model improved the error rate from 15% to 3% for point estimates, providing a predictive… Show more Development and implementation of data science and machine learning solutions for business decision-making, primarily related to product opportunity and financial health, using cloud services such as AWS, Azure, and Databricks for robust and reliable implementation.★ Key Achievements ★⇨ Designed a forecasting model to predict revenue using the Dora Framework and AWS for deployment. The new model improved the error rate from 15% to 3% for point estimates, providing a predictive interval and modernizing the previous model.⇨ Developed a regression model to understand the key characteristics affecting revenue growth, aiding in the creation of business rules.⇨ Created an ETL using Databricks to integrate databases and automate a process that was previously done manually by the client.⇨ Actively participated in the creation of a data science team at the client, standardizing documentation, processes, and meetings for knowledge exchange, as well as presenting machine learning topics to enhance the knowledge and integration of the client's data scientists. Show less

Creation and calibration of mortality table models for companies in the insurance and reinsurance market; Mongeral Aegon, Sicoob, Bradesco Seguros.★ Key Achievements ★⇨ Creation of mortality tables using p-spline and dynamic systems combined with IBNR (incurred but not reported) techniques, supporting the financial health of products and business decisions involving them.⇨ Seminar at the UFRJ's Scientific Initiation Journey on the modeling method using dynamic systems.

Main activities: Private lessons and tutoring for elementary and high school students. As well as preparation for university entrance exams.