Dmitri Ganjour is a highly analytical professional with a Master of Science in Applied Mathematics from ETH Zürich and a Bachelor of Science in Mathematics from EPFL. His expertise spans probability theory, stochastic calculus, financial mathematics, and computational methods for finance.

His master’s thesis focused on machine learning methods for derivatives pricing and hedging, with an emphasis on model calibration and stochastic model approximation. Using Python, PyTorch, SciPy, and Sklearn, he developed numerical implementations for derivative pricing and risk management. He also authored research on adaptive robust control and mean-variance portfolio optimization. At the University of Amsterdam, he contributed to neural network research, studying the convergence of deep learning models.

Dmitri transitioned into finance as a derivatives trader, specializing in freight, grain, and fertilizer markets. He has experience trading freight derivatives (FFA, fuel oil, and gasoil swaps), managing hedging and speculative positions, and conducting market analysis across commodities. At Fertistream Freight DMCC and Arion Commodities in Dubai, he developed systematic trading strategies, leveraging quantitative modeling and risk management expertise..

Passionate about data analysis, algorithmic development, and financial modeling, Dmitri continuously refines his approach to market prediction and portfolio optimization. Fluent in Russian, French, and English, and proficient in Python, C++, R, and MATLAB, he combines technical excellence with practical financial acumen in the evolving landscape of quantitative finance and trading.