Charles Auguste
Quantitative Trader
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolCharles Auguste is a quantitative trader based in London with a decade of hands-on experience applying applied mathematics, machine learning and software engineering to market-making and systematic equity strategies. Trained at École des Ponts ParisTech and ENS Paris-Saclay (MVA), he blends a rigorous academic background in ML and financial engineering with production trading experience at Tower Research and SIG. His work spans derivative pricing, Monte‑Carlo simulation, constrained gradient-boosting research and building fast C++/Python tooling for trading desks. Comfortable bridging research and execution, he has repeatedly translated academic techniques into robust, low-latency trading workflows. A less obvious strength is his early revenue-management and data-toolbuilding experience, which sharpened his optimization and product-focused instincts beyond pure quant research.
10 years of coding experience
Engineer's degree, Applied Mathematics, Computer Science, Finance, Engineer's degree, Applied Mathematics, Computer Science, Finance at Ecole Nationale des Ponts et ChausséesBaccalaureate in Sciences with highest Honours, Mathematics, Life Sciences, Physics, Baccalaureate in Sciences with highest Honours, Mathematics, Life Sciences, Physics at Lycée Jean Zay (Orléans, France)Mathematics, physics, computer sciences, Mathematics, physics, computer sciences at Lycée Pothier (Orléans, France)MVA Master's degree, Machine learning, Mathematics, Computer Science, MVA Master's degree, Machine learning, Mathematics, Computer Science at École Normale Supérieure Paris-Saclay
French, English, Spanish, Japanese
Github Skills (29)
gbm10
catboost10
kaggle10
parallel10
python10
microsoft10
classification10
r10
data-mining10
machine-learning10
gradient10
gradient-boosting10
boosting10
lightgbm10
ranking10
Programming languages (3)
Github contributions (5)
CharlesAuguste/LightGBM
Jul 2019 - Jan 2021
A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
Contributions:2 PRs, 86 pushes, 20 branches in 1 year 6 months
microsoftgbmclassificationumbrellagradient-boosting
AmineKheldouni/IMI---Department-Project
Feb 2017 - May 2017
Contributions:18 commits, 15 pushes, 2 branches in 2 months
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