Tokuma Suzuki
データサイエンティスト at 株式会社JDSC
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolTokuma Suzuki is a data scientist based in Tokyo with eight years of experience applying machine learning, causal inference, and NLP to manufacturing and financial domains. He combines academic training in macroeconomics from the University of Tokyo and a business background from Keio with hands-on ML deployment experience at JDSC and research-driven model development at Mitsubishi UFJ’s MTEC. Tokuma has shipped practical models in production contexts and contributed to open-source scientific computing by adding backward induction support to QuantEcon.jl, demonstrating care for edge cases like beta=1. Equally comfortable with cloud platforms (AWS, GCP) and software engineering practices, he bridges economic intuition and technical rigor to turn complex data into actionable decisions.
8 years of coding experience
修士, 経済学, 修士, 経済学 at 東京大学学士（商学）, 学士（商学） at 慶應義塾大学
Github Skills (8)
algorithm10
unit-testing10
algorithms10
dynamic-programming10
implement10
julia10
numerical-methods9
back-end-development9
Programming languages (8)
Github contributions (5)
QuantEcon/QuantEcon.jl
Dec 2018 - Dec 2018
Julia implementation of QuantEcon routines
Role in this project:
Back-end Developer
Back-end DeveloperContributions:15 commits, 1 PR, 5 comments in 7 days
Contributions summary:Tokuma primarily focused on implementing and testing a backward induction algorithm within the `quantecon.jl` repository, which is a Julia implementation of QuantEcon routines. Their contributions involved adding the backward induction functionality, including tests, and ensuring its correct behavior. The user also addressed edge cases, specifically regarding beta=1, adding relevant checks and tests. Overall, the commits suggest a focus on extending the library's dynamic programming capabilities.
econometricsroutineseconomicsjulia
tokuma09/DeZero
Aug 2020 - Aug 2020
Contributions:106 PRs, 58 pushes, 111 branches in 27 days
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