Data Scientist specializing in the development of data-driven products aimed at enhancing revenue and reducing costs through Data Analysis, Business Intelligence, and Machine Learning techniques. Expertise includes product classification, Key Value Item selection through customer behavior analysis, and the application of Graph Theory in analytical processes. Proficient in managing product recommendation models, calculating performance metrics, and automating processes using SQL, Python, PySpark, and visualization tools such as Power BI.

Previous experience includes developing Lifetime Value (LTV) calculation models and business metrics projection models for customer acquisition and retention. Created dashboards using Shiny R for metric consultations and analyzed advertisement outcomes using R language and RStudio.

Educational background includes an MBA in Data Science & Analytics, with a focus on Machine Learning techniques such as Clustering, Correspondence Analysis, Factorial Analysis (PCA), GLM/GLMM Regressions, Time Series Analysis, and Tree Models, along with a strong foundation in Statistics and Hypothesis Testing. Currently pursuing further education in Technology for Business: AI, Data Science & Big Data while actively engaging in ongoing studies within the Data Science community.

Research experience includes two years in Field Theory during undergraduate studies, culminating in three published scientific papers internationally. Master's degree research focused on Particle Physics, resulting in participation in an international publication. The overarching goal is to excel as an independent Data Scientist, devising solutions that significantly enhance business outcomes.