Personal details
Samuel T.
Technical skills
About
As a Senior Data Analyst at iFood, a leading food delivery company, I leverage my skills in Python, Databricks, and Tableau to enhance user engagement and experience by implementing and validating app changes through A/B tests. I also monitor and analyze key business metrics, such as user retention, sales, and access data, and propose new testing initiatives to optimize them.
Before joining iFood, I was a Data Scientist at Visagio, a technology and management consulting firm, where I contributed to successful projects across diverse sectors, such as retail, banking, and energy. I gained experience in time series forecasting, classification, and regression, as well as participated in the development of scalable NLP tools, such as a Python library for AI model management. Additionally, I have over 5 years of experience in data science research, having engaged in two scientific projects related to machine learning. I am currently pursuing my Master's degree in Artificial Intelligence at USP, where I also obtained my Bachelor's degree in Mechatronics Engineering.
Work Experience
- Development of a decision-making system for determining which cities to focus the efforts of the sales team;
- Identifying opportunities for improving components of a mobile application with millions of users.
- Evaluating business KPIs using technologies such as Python, Tableau, SQL, and Databricks.
- Development of a Python library for the management, execution, and results monitoring of AI models. This software was used in 3 different NLP products, a time series forecasting, and a recommendation system.
- Demand forecasting and inventory optimization system for a retail company with 1,700 stores across Brazil. This system was developed using python.
- Prediction system to assist in the decision of when and where we should increase cashback in a network of over 6,000 gas stations. The system was developed on Databricks using Python.
- Maintenance and monitoring of the results of a system for predicting the amount of money to be withdrawn from ATMs in a network of banks with over 2,000 branches in Brazil.