Rafael Rangel

Rafael Rangel has a diverse background in data science and education. Rafael has recently worked as a Data Scientist at ZENVIA, where they were involved in data collection, pre-processing, and storage from various sources. Rafael applied statistical techniques and data modeling to perform data analysis, built reports and dashboards using Google Data Studio, and implemented natural language processing (NLP) solutions using pre-trained models like ChatGPT. Rafael also used big data tools like Databricks to process large volumes of data and developed features for enterprise platforms, including building AI-based chatbots.

Prior to their role at ZENVIA, Rafael worked as a Teacher at the National Telecommunications Institute - Inatel. Within the Inatel Casa Viva project, they taught Information Technology classes, prepared teaching material, developed and prototyped teaching technology projects, and programmed gamification elements.

Before that, Rafael had practical research experience as a Pibic/CNPq Scientific Initiation Scholarship recipient at CNPq. Rafael'sresearch focused on studying open clusters using machine learning techniques, specifically creating a classifier using neural networks. Rafael also worked on a project involving a morphological classifier of galaxies, using various machine learning models and Python programming. This project served as their course conclusion work.

Overall, Rafael's work experience showcases their expertise in data science, education, and research, with a strong emphasis on machine learning and data analysis.

Rafael Rangel is a physicist with a strong focus on data science, big data analytics, and astrophysics. Rafael obtained their Bachelor of Physics degree from Universidade Federal de Itajubá in 2021. Following that, they pursued further education and completed a Master's degree in Physics with a specialization in Astrophysics from the same university, which lasted from 2022 to 2024. During their academic journey, Rafael also pursued additional certifications to enhance their skills. Rafael obtained certifications in various areas such as data processing, machine learning, natural language processing, and cloud infrastructure from institutions like Alura and Google. Additionally, they also attended a course in artificial intelligence and its applications in physics at the Centro Brasileiro de Pesquisas Físicas - CBPF. Rafael's educational and certification background showcases their commitment to leveraging their physics background and applying data science techniques in their field of expertise.