I am a data scientist with 6 years of experience, specializing in statistics. My skills cover data analysis, extracting actionable insights, predictive modeling, and crafting innovative solutions.
I excel in decoding complex datasets to uncover valuable trends, and I’m adept at using predictive modeling to foresee future patterns and optimize strategies. My strength lies not just in understanding data but in turning insights into practical solutions.
Passionate about continuous learning, I stay updated on the latest in data science to ensure I bring the most effective strategies to the table. I approach my work with a clear focus on using data to drive precision, efficiency, and innovation.
May 2019 - Apr 2024
São Paulo
Catho is a leading Brazilian online platform that connects job seekers with employers, offering a wide range of career opportunities across various industries.
Sep 2021 - Apr 2024
May 2019 - Aug 2021
Aug 2018 - Apr 2019
São Paulo
Avalia Educacional, part of Grupo Santillana, is a leading educational assessment provider in Latin America, having reached around 10 million students across 22 countries.
Aug 2018 - Apr 2019
Dec 2017 - Jun 2018
São Paulo
Fundação Vunesp is responsible for organizing and conducting large-scale educational assessments and public selection processes in Brazil.
Dec 2017 - Jun 2018
2023-2025 M.Sc. in StatisticsTaken Courses:
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2013-2017 B.Sc. in StatisticsTaken Courses:
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This project is dedicated to the study, training, and storage of machine learning models. It provides Jupyter notebooks for model exploration and training, as well as a structure for storing datasets and trained models ready for deployment. These models can be utilized by the Machine Learning API project for serving predictions and model-based services.
This project aims to create a Python API using FastAPI and deploy it to AWS using AWS Lambda, and it is designed to serve predictions based on models imported from an external repository: Machine Learning Models Repository. The application is containerized using Docker to provide an isolated and reproducible development environment. AWS SAM CLI is utilized for local testing, building, and deploying the serverless application to AWS. The project demonstrates how to efficiently build and deploy a scalable API with FastAPI on AWS Lambda, leveraging the simplicity and flexibility of Docker and AWS serverless services.
This project provides a Machine Learning API that serves machine learning models through a robust and scalable web interface. The API is built using Docker and Docker Compose and is designed to serve predictions based on models imported from an external repository: Machine Learning Models Repository.
This project sets up a full observability stack to keep an eye on and analyze your Machine Learning (ML) Model API. Using Elasticsearch, Kibana, Logstash, Filebeat, Metricbeat, and Heartbeat, it makes it easy to collect, explore, and visualize logs and metrics. Everything runs with Docker Compose to manage all the containerized services smoothly, based on the Elastic Stack (ELK) on Docker. It’s designed to monitor and support the Machine Learning API Environment, giving you better insights and performance tracking.