Matheus Facure
Staff Data Scientist
Brazil
Summary
🤩
Rockstar🎓
Top SchoolMatheus Facure is a Staff Data Scientist with a decade of experience applying econometrics and machine learning to real-world product problems at Nubank, where he progressed from Data Scientist to Data Science Manager and now leads causal work. He is the author of Causal Inference in Python and the popular "python-causality-handbook," translating academic econometric methods into practical tooling and tutorials that reach practitioners worldwide. His open-source contributions include adding non-parametric double/debias machine learning and causal validation features to Nubank’s fklearn, improving robustness of causal effect estimation in production. As an educator he taught ML and Python to MBA students, reflecting a talent for making complex ideas accessible and memorable—sometimes via memes. Based in Brazil and trained in economics and game theory at Universidade de Brasília, he blends rigorous causal thinking with product-focused experimentation. Colleagues know him for pushing causal inference from niche research into mainstream data science workflows.
10 years of coding experience
4 years of employment as a software developer
Economia, Aprendizado de Máquina, Análise de Dados, Teoria dos Jogos, Economia Comportamental., Economia, Aprendizado de Máquina, Análise de Dados, Teoria dos Jogos, Economia Comportamental. at Universidade de Brasília
Portuguese, English
Stackoverflow
13reputation
649reached
1answer
2questions
Github Skills (19)
python10
data-science10
scikit10
statistics10
pandas10
machine-learning10
econometrics10
causal-inference10
regression-models10
statistical-models10
scikit-learn10
data-analysis10
ml9
numpy8
testing8
Programming languages (5)
Github contributions (5)
matheusfacure/python-causality-handbook
Apr 2020 - Sep 2022
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 72 reviews, 214 commits in 2 years 5 months
Contributions summary:Matheus's contributions focus on developing and documenting causal inference techniques within the repository. They added introductory content explaining the relevance of causal inference and its relation to machine learning. Their work includes implementing practical examples using simulated data to illustrate core concepts like randomised experiments, difference-in-differences, and synthetic control methods. Additionally, the user provides references to the underlying econometric literature to support their explanations, with an aim to make association be causation.
pythoncausalbraverigorousstatistics
nubank/fklearn
Oct 2021 - Sep 2022
fklearn: Functional Machine Learning
Role in this project:ML Engineer
ML EngineerContributions:7 reviews, 11 commits, 6 PRs in 10 months
Contributions summary:Matheus's primary contributions involve the development and refinement of causal inference functionalities within the `fklearn` repository. They focused on implementing and testing causal validation functions, including area under the cumulative effect curve calculations and debiasing techniques with regression models. Furthermore, the user added non-parametric double/debias machine learning capabilities. This involved refactoring code and addressing type-related issues, enhancing the library's usability and expanding its application within the domain of functional machine learning.
data-analysispythondata-sciencemlmachine-learning
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