My practical experience includes data analysis and modeling of large datasets. At Cloudwalk, I have gained extensive hands-on experience with Python, SQL, Looker, and various Python libraries such as Scikit-learn, pandas, and numpy. A notable accomplishment in my current role is the development of a churn detection machine learning model aimed at identifying early signs of client churn based on their behavioral data.
I hold a Bachelor’s degree in Physics from the Federal University of Rio de Janeiro (UFRJ) and a Master’s degree in Particle Physics from the Brazilian Center for Physics Research (CBPF), in collaboration with CERN, specializing in Data Analysis. In 2023, I graduated from a Data Science bootcamp at Le Wagon and have since been employed at Cloudwalk Inc., first as a Data Analyst and now as a Data Scientist.
Skills: Python, SQL, NumPy, Pandas, ScikitLearn, Machine Learning, and Deep Learning.
Data analysis of charge asymmetries in charm decays, looking for evidences of CP violation
Skills: C++, ROOT, LaTeX, Python
After being succesful in churn modeling and prediction, was awarded with the position of Data Scientist, being responsible for most churn-related initiatives of the company's R&D team. Also takes part on the Lifecycle team, focused on data driven solutions for activation, retention and attraction of clients.
Specialized in interpreting complex datasets, applying statistical techniques for in-depth analysis, and identifying emerging trends. A key project involved developing and refining a churn prediction model using CatBoost architecture. This model became pivotal in targeting merchant communications effectively. My responsibilities spanned the entire model lifecycle—from feature engineering and model tuning to deployment. Proficient in Python, SQL, and data analytics platforms including Google BigQuery, MetaBase, and Looker.
Conducted comprehensive data analysis on charmed three-body decays utilizing LHCb dataset. My role involved meticulous data cleaning and sophisticated modeling to detect particle-antiparticle asymmetry—an essential element in probing CP asymmetry in the universe. This research required deep knowledge of physics principles, precise data fitting, and advanced statistical analysis to ensure accurate interpretation and findings. Experience with C++, ROOT and Python.
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