Juan Loyola
Machine Learning Engineer at ICBC Argentina
San Luis, Argentina
Summary
👤
Senior🎓
Top SchoolJuan Loyola is a Machine Learning Engineer and Computer Science PhD with 12 years of experience building end-to-end ML solutions, currently applying his expertise at ICBC Argentina. He combines industry work in fraud detection and document classification with academic research on early classification and reproducible ML pipelines developed during his PhD and CONICET fellowship. Proficient in Python, SQL and tools like PyTorch, PyMC and scikit-learn, he contributes to major open-source projects—adding prediction/refitting features to PyMC and improving tests and documentation in scikit-learn. A seasoned educator and team player, he enjoys mentoring, designing robust model monitoring/serving systems, and delving into problems from research to production. Fluent in English and Spanish and active in the open-source community, he often balances collaborative code review with solo research-driven implementations.
12 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Universidad Nacional de San Luis
Spanish, English
Stackoverflow
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Github Skills (16)
scikit10
statistics10
mcmc10
pymc10
machine-learning10
bayesian10
probabilistic-programming10
python10
bayesian-inference10
statistic10
scikit-learn10
data-science9
pytest9
variational-inference9
data-analysis8
Programming languages (8)
Github contributions (5)
scikit-learn/scikit-learn
Jun 2021 - Feb 2022
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:63 reviews, 94 commits, 53 PRs in 7 months
Contributions summary:Juan primarily focused on improving the codebase's quality and maintainability within the scikit-learn machine learning library. Their contributions involved removing unused variables, a common practice in code optimization, and replacing the `assert_warns*` functions with pytest context managers within the testing suite. Furthermore, the user worked on ensuring the documentation of several machine learning models, like the Multi-layer Perceptron Classifier (MLPClassifier) and Nearest Neighbors passed numpydoc validation, contributing to the project's adherence to documentation standards.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
pymc-devs/pymc
Mar 2019 - Feb 2022
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:Data Scientist
Data ScientistContributions:19 commits, 8 PRs, 10 comments in 3 years
Contributions summary:Juan contributed to the Bayesian Modeling and Probabilistic Programming in Python project by adding features to model prediction and refitting capabilities. Their work involved code modifications within the documentation, including notebook updates and updates for Bayesian Neural Network ADVI. They also implemented the `set_data` function, and removed the `refit` function as part of this process.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
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