Developed machine learning models (random forest, XGBoost, etc) to identify money laundering client accounts, alerting around 90 atypical behaviors per month with up to 80% precision. Developed and maintained over 100 money laundering rules. Built dashboards to monitor transactions made for betting companies to ensure current laws are followed (no credit card transactions). Built a dashboard to monitor biometric calls, providing information of which areas/products are called the most to possibly migrate them to a zero-cost process. Conducted and assisted in studies by gathering, extracting, and compiling data to provide valuable business insights, while formatting, restructuring, and validating the collected data to ensure quality and accurate results.

Sr. Data Scientist - CoE xTech Data Science at BIP Brasil

October 1, 2023 - October 1, 2024

Led validation of satellite image classification model for agriculture to better track farmers' crop planting strategy and suggest a more ecological/carbon efficient one. Developed a price-demand time series prediction model for an oil company, saving up to USD 100,000/year (back test). Assisted/helped around 12 coworkers daily in applying data science to each company's business.

Data Scientist - Data Science Squad at Virtualisurg

March 1, 2022 - September 1, 2023

Led the data science squad to develop evaluation methods of medical procedures and machine learning models for VR trajectories to compare trainees and expert doctors' simulation performance. Created the workflow alongside back-end squad for model deployment. Attended the AFC Congress in Paris to showcase virtual reality medical training sessions to attendees. Developed (front end) the webpage for dashboards.

Scientific Undergraduate Research at University of São Paulo

June 1, 2021 - February 1, 2022

Conducted analysis comparing LSTM Neural Network and ARIMA time series models performance for stock prices.