Quoc Trinh is a Data Science Manager based in Paris with 8 years of experience applying machine learning, quantitative finance, and automation to high-impact production systems. He has driven major efficiency gains—automating pricing pipelines to cut quoting from weeks to hours and boosting annual premiums by ~50%—and built a risk framework that reduced portfolio risk calculation from days to minutes. Comfortable from research to engineering, he’s delivered scalable ML systems for wildfire detection, video deduplication at scale, and robotic control, and has a track record of cutting training and processing times by an order of magnitude. An active open-source contributor, he’s improved code quality and fixed subtle edge-case bugs in pandas while adding testing templates to Exercism’s Python exercises, reflecting his emphasis on robustness and reproducibility. With dual masters in Data Science for Finance and Financial Engineering, he combines breadth-first curiosity with the willingness to go deep when problems demand it.
8 years of coding experience
4 years of employment as a software developer
Master of Engineering - MEng Data Science for Finance, Master of Engineering - MEng Data Science for Finance at IMT Atlantique
Master of Science - MS Financial Engineering, Master of Science - MS Financial Engineering at WorldQuant University
Bachelor's degree Telecommunications Engineering, Bachelor's degree Telecommunications Engineering at Ho Chi Minh City University of Technology
Contributions:13 commits, 17 PRs, 26 comments in 8 months
Contributions summary:Quoc's contributions primarily involve adding test templates to various Python exercises within the Exercism repository. These templates facilitate the creation of unit tests, covering scenarios for exercises such as resistor-color, beer-song, allergies, alphametics, anagram, atbash-cipher, ocr-numbers, phone-number, custom-set, and dnd-character. The user's work streamlines the testing process for new exercises and ensures the correctness of solutions. The commits focus on adapting and implementing testing frameworks for a variety of Python exercises.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
Backend Developer
Contributions:16 reviews, 45 PRs, 66 comments in 4 years 8 months
Contributions summary:Quoc primarily focused on code style improvements and bug fixes within the pandas library. They addressed code style issues by enabling specific pylint rules and automating code formatting with ruff. Furthermore, the user corrected errors in docstrings and fixed bugs related to data handling, including cases with nullable integers and timestamp-related issues. The contributions involved modification of pandas' internal code, focusing on improving code quality and addressing edge cases.
pythondatalabeled-datamanipulationdataframes
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