Summary
Gaël Pagès is a Chief Science Officer and machine learning strategist with eight years of applied AI and quantitative experience focused on medium-frequency trading in commodities and financial markets. He has built and led systematic trading strategies and data science teams at Louis Dreyfus Company and ECTP, combining neural networks, genetic programming, HMMs and other techniques to forecast pricing and production drivers. Based in Geneva, he also teaches Python and quantitative methods to finance students at ESCP and serves on the board of an AI startup, bridging academic instruction with product-focused research. His background in mining engineering and specialized finance gives him uncommon domain depth in weather, crop yield and commodities fundamentals alongside advanced statistical modeling. Comfortable moving models from research into production, he favors explainable, decision-oriented analytics that directly inform trading decisions.
8 years of coding experience
13 years of employment as a software developer
Erasmus exchange semester, Mining Engineering, Ramon Querol diploma - Exploration and Production of Hydrocarbons, Erasmus exchange semester, Mining Engineering, Ramon Querol diploma - Exploration and Production of Hydrocarbons at Universidad de Oviedo
Nanodegree, Artificial Intelligence, Nanodegree, Artificial Intelligence at Udacity
Mathematics and Physics, Mathematics and Physics at Lycée Pierre de Fermat
Specialized Master in Finance, Finance, Commodities Trading, Statistics, Economics, Quantitative, Option & Asset Pricing, Corporate, Specialized Master in Finance, Finance, Commodities Trading, Statistics, Economics, Quantitative, Option & Asset Pricing, Corporate at ESCP Europe
MSc in Mining Engineer, Mathematics, Statistics, Geostatistics, Commodities, MSc in Mining Engineer, Mathematics, Statistics, Geostatistics, Commodities at Ecole des Mines d'Alès
french - native, english - fluent, spanish - fluent