Naoya Kanai
Data Engineer, Reality Labs
California, United States
Summary
🤩
Rockstar🎓
Top SchoolNaoya Kanai is a data engineer at Reality Labs with 12 years of experience applying data science and machine learning to high-impact product problems. He has driven production fraud-detection systems and experimentation at scale during a six-year tenure at Airbnb, reducing fake inventory and co-authoring published work on variance reduction in A/B tests. His open-source contributions to cornerstone projects like scikit-learn and matplotlib show a practical focus on robustness—improving algorithms, documentation, and test infrastructure—and signal strong attention to quality and reproducibility. Comfortable across backend systems, Spark pipelines, and model deployment, he pairs a consulting background with hands-on engineering and a rare arts-and-science education that includes cello performance studies at Juilliard and a BA in Human Biology from Stanford.
12 years of coding experience
12 years of employment as a software developer
Master of Music Cello (Performance), Master of Music Cello (Performance) at The Juilliard SchoolColumbia UniversityBA Human Biology, BA Human Biology at Stanford University
English, Japanese, Chinese, German
Github Skills (30)
algorithm10
markdown10
image-comparison10
python10
data-science10
matplotlib10
testing10
scikit10
nltk10
machine-learning10
machine-learning-algorithms10
numpy10
markdown-it10
natural-language-processing10
scikit-learn10
Programming languages (8)
Github contributions (5)
airbnb/knowledge-repo
Sep 2018 - Aug 2021
A next-generation curated knowledge sharing platform for data scientists and other technical professions.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 reviews, 58 commits, 34 PRs in 2 years 10 months
Contributions summary:Naoya primarily focused on updating and maintaining the backend infrastructure of the knowledge-repo platform. Their contributions include fixing issues related to markdown versions and updating the metadata for Python versions to support multiple Python versions. They also made changes to support the deprecation of Python 2 and refactored code for compatibility with modern Python versions. Additionally, the user worked on improving the platform's codebase through import cleanup and code deprecation tasks.
scientistsnext-generationdata-analysisdatadata-science
scikit-learn/scikit-learn
Aug 2016 - Aug 2018
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:18 commits, 28 PRs, 141 comments in 2 years
Contributions summary:Naoya contributed to the scikit-learn library by implementing and improving various machine learning functionalities. Their work included adding support for additional parameters in the DBSCAN clustering algorithm, deprecating a parameter in SparsePCA, and updating dataset URLs. Additionally, the user updated the documentation, upgraded jQuery in the HTML documentation, and improved the robustness of the library by updating NumPy references and removing deprecated features and dependencies. Their contributions focused on enhancing existing algorithms, improving documentation, and ensuring the library's compatibility with the latest dependencies.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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