Gabriel Azevedo
Research Engineer at Google DeepMind
Singapore
Summary
🤩
Rockstar🎓
Top SchoolGabriel Azevedo is a research engineer based in Singapore with six years of experience at the intersection of machine learning research, software engineering, and data-driven strategy. He has held roles across DeepMind, Google, and McKinsey/QuantumBlack, where he translated causal inference and probabilistic methods into production-ready solutions for high-impact clients. Gabriel contributes to open-source causal inference tooling (notably improving tutorials and adding algorithmic constraints to McKinsey’s causalnex repo), reflecting a focus on usable research and developer experience. His background combines rigorous academic training from École Polytechnique and NUS with hands-on system building in healthcare and defense research contexts. Comfortable bridging research, engineering, and management, he often prioritizes clarity and reproducibility when moving models from notebooks to deployed pipelines. An under-the-radar strength is his knack for simplifying complex algorithms for broader adoption without sacrificing mathematical rigor.
6 years of coding experience
7 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at École PolytechniqueBachelor's degree Computer Science, Bachelor's degree Computer Science at Instituto Tecnológico de Aeronáutica - ITAMaster of Engineering - MEng, Master of Engineering - MEng at National University of Singapore
French, English, Portuguese, Spanish
Github Skills (8)
bayesian-network10
machine-learning10
python10
data-science10
causal-inference10
bayesian-inference9
numpy9
documentation8
Programming languages (2)
Github contributions (5)
mckinsey/causalnex
May 2020 - Jan 2023
A Python library that helps data scientists to infer causation rather than observing correlation.
Role in this project:
Data Scientist
Data ScientistContributions:4 releases, 26 reviews, 24 commits in 2 years 8 months
Contributions summary:Gabriel contributed to the `causalnex` repository by modifying tutorial notebooks to simplify syntax and enhance clarity for users. Their commits included code changes in the documentation, indicating a focus on improving the user experience and ease of understanding. They also added a non-negativity constraint in the numpy lasso implementation and integrated DYNOTEARS, demonstrating a strong focus on causal inference methods within the library.
scientistsfairness-mlcausal-modelspythoninfer
GabrielAzevedoFerreiraQB/causalnex
Apr 2020 - Jul 2022
A Python library that helps data scientists to infer causation rather than observing correlation.
Contributions:91 pushes, 8 branches in 2 years 2 months
python-libraryscientistspythoninferdata-science
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.