José Neto
Data Engineer
Greater Brasilia Brazil
Summary
👤
Senior🎓
Top SchoolJosé Neto is a Data Engineer with nine years of experience and Google Professional Data Engineer certification, currently running data pipeline design, monitoring and BI reporting for Brazil's national petroleum regulator. He works across the modern data stack—Python, SQL, dbt, Airflow, BigQuery and Snowflake—and has built production ETL, orchestration and Spark processing on Google Dataproc. José has applied ML and NLP in government settings to turn citizen feedback and legal texts into actionable insights, and contributed to the popular PyCaret project by adding custom MLflow experiment tagging for better experiment provenance. With a background in chemistry and soil science, he brings rigorous analytical thinking to data quality and simulation work (including Monte Carlo scenarios) for large national programs. Based in the Federal District (Brazil), he also mentors teams on data engineering and ML operationalization.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS Chemistry, Bachelor of Science - BS Chemistry at Universidade Federal de Santa CatarinaMaster’s Degree Soil Science, Master’s Degree Soil Science at Universidade do Estado de Santa Catarina
English, Portuguese
Stackoverflow
33reputation
956reached
0answers
1question
Github Skills (16)
machine-learning10
mlflow10
pycaret10
python10
classification10
data-science9
mlops9
loops6
counter6
anomaly-detection5
clustering5
regression5
time-series4
pytorch4
tensorflow4
Programming languages (6)
Github contributions (5)
pycaret/pycaret
Aug 2021 - Jan 2022
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:1 review, 7 commits, 1 PR in 5 months
Contributions summary:José contributed to the `pycaret/pycaret` repository by adding the `experiment_custom_tags` parameter to several functions, including `setup`, `compare_models`, `create_model_unsupervised`, `create_model_supervised`, `finalize_model`, `_mlflow_log_model`, and others. They also modified the test suite to include a test for the classification module. These changes suggest a focus on integrating custom metadata logging into the MLflow tracking system, enhancing the experiment tracking capabilities of the PyCaret library.
automllow-codepycaretpythonreact
netoferraz/udacity
Feb 2017 - Aug 2017
Contributions:214 pushes, 2 branches in 5 months
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