Quentin Batista
Applied Scientist at Amazon
Tokyo, Japan
Summary
🤩
Rockstar🎓
Top SchoolQuentin Batista is an applied scientist with nine years of quantitative research and engineering experience, currently building ML-driven solutions at Amazon in Tokyo. He blends rigorous academic training—PhD work in Finance at MIT and an MA in Economics from the University of Tokyo—with hands-on quant roles at asset managers and research institutes, translating economic models into robust production tooling. Quentin’s background spans econometric modeling, algorithmic game-theory testing, and automated QA—he contributed extensive unit tests to the widely used QuantEcon Python library, improving reliability across distributions, ARMA models, and game-theory solvers. Comfortable at the intersection of research and product, he has a track record of shipping validated, test-covered code that bridges theoretical models and real-world systems. Fluent in global research environments from Montreal to Tokyo, he brings a pragmatic, test-first approach to complex, data-driven problems.
8 years of coding experience
1 year of employment as a software developer
PhD in Finance, PhD in Finance at Massachusetts Institute of TechnologyUniversity of TokyoExchange Program, Exchange Program at Hitotsubashi UniversityJoint Honours in Economics and Finance, Joint Honours in Economics and Finance at McGill University
English, French, Japanese
Github Skills (6)
unit-testing10
python10
numpy10
test-automation10
game-theory9
continuous-integration6
Programming languages (3)
Github contributions (5)
QuantEcon/QuantEcon.py
Aug 2017 - Mar 2019
A community based Python library for quantitative economics
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:7 reviews, 21 commits, 22 PRs in 1 year 7 months
Contributions summary:Quentin primarily contributed to the quality assurance of the `quantecon/quantecon.py` repository through the addition and modification of unit tests. The commits focused on testing functionalities related to distributions, game theory algorithms (Lemke-Howson, support enumeration, vertex enumeration, and mclennan_tourky), ARMA models, and timing functions. Their work ensured the robustness and reliability of the code base by covering multiple modules with new and improved tests.
python-librarycommunity-basedpythondata-sciencemachine-learning
QBatista/Games.jl
Aug 2017 - Apr 2021
Algorithms and data structures for game theory in Julia
Contributions:31 pushes, 14 branches in 3 years 8 months
algorithms-and-data-structuresgame-theorytheoryjuliadata-structures
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