We created REST and SOAP API extraction scripts using AWS serverless services to bring together data from different countries and systems into a single platform that allows information to be visualized through KPIs, OKRs and financial indicators. During my contract we developed up to 30% of the entire project being 1 month ahead of the calendar.

As a data scientist, I worked with different supervised models created to help the operations team improve their KPIs. One of the most important models created was a random forest model to identify customers with the highest probability of payment. Due to the nature of the business, three different models were created for different types of products and on average increased resource collection by 15%.

- As a Softwate/ Python developer I supported the QA area to automate different types of processes. There used to be a lot of data extraction processes from the web and different client platforms. A big part of my job in this work was to understand the QA process and generate a tool that automated data extraction and file management using Python and libraries such as Selenium, Beautiful Soup, OS and Pandas