Trained machine learning models in Python to get specific texts from documents of investment funds.Developed Python scripts to obtain texts from multiple machine learning models and store the information in a PostgreSQL database hosted in AWS RDS.

Refactored a backend in Python that was previously written in Excel (VBA) to optimize the daily schedule of Leroy Merlin’s employees in Brazil (+30 shops) based on customer traffic in store.The employees scheduling problem constraints were: store opening and closing times, day and night shifts, time scale (15 minutes, 30 minutes or 1 hour), minimum number of employees per time scale. Workforce profiles contained: hours of daily work time, minimum and maximum workers from profile, amount of time to eat and minimum work time before and after eating.Developed an API to receive an input from the frontend with shop constraints to the backend return a json file containing the employees schedule ready and optimized.The Python backend returned the result in less than one second while the Excel (VBA) software took minutes.

Developed KPI dashboards in Excel (VBA) in order for the team to manage their goals for the week and month. Performance metrics were: how many documents were returned or not, how many missed their deadline or not, amount of documents to soon miss the deadline, clients that request most legal papers, areas of request, etc.Prototyped a machine learning model to predict which documents would be found or not to optimize and automate part of the team's workflow.