Alesson Santos da Silva

Senior Data Scientist at the financial institution Itaú Unibanco. Graduated in Production Engineering from the School of Engineering of Lorena at USP (EEL USP) and holds an MBA in Data Science from the Institute of Mathematics and Computer Science at USP (ICMC USP). During his undergraduate studies, he participated in several extension projects with different institutions. Among them, the following stand out: a culture and extension project with a PUB-USP scholarship in the area of modeling scientific games for classroom use, under the guidance of Professor Gabrielle Weber Martins, and technical training and scientific initiation projects with a FAPESP scholarship in the area of bioproduct production (xylitol and ethanol) from various types of biomass, under the guidance of Professor Silvio Silvério da Silva. He completed a research internship abroad with a scholarship provided by FAPESP, which consisted of a collaboration between Professor Silvio Silvério da Silva's bioprocess laboratory at the School of Engineering of Lorena at the University of São Paulo and Professor Sudip Kumar Rakshit's biorefinery laboratory at Lakehead University (Thunder Bay, Ontario, Canada). He presented thesis projects for his undergraduate and MBA degrees with the following topics, respectively: "Use of undersampling techniques and gradient boosting in modeling a binary classification problem with imbalanced data," under the guidance of Professor Mariana Pereira de Melo, and "Application of ensemble algorithms in evaluating strategies used for handling imbalanced classes," under the guidance of Professor Adriano Kamimura Suzuki. Currently, he works as a senior data scientist at Itaú Unibanco in the credit risk modeling and retail billing area for SMEs, utilizing tools such as Python, SQL, Amazon Web Services, Apache Spark, Apache Hadoop, Git, GitHub, and Microsoft Office Suite. His tasks encompass ETL processes, preprocessing, statistical analysis, modeling, and application of machine learning algorithms in solving business problems. Through his academic and professional experiences, he has consolidated knowledge in various technology fields, such as: the use of supervised and unsupervised machine learning methods; application of Python libraries such as matplotlib, numpy, scikit-learn, imbalanced-learn, seaborn, scipy, statsmodels, and xgboost; deployment of models using AWS tools; and automation of ETL processes with AWS Glue and PySpark. (Source: Lattes Curriculum)