Rhayoute Abdelmalek

Validated and extended the Vanna-Volga method for exotic FX options, improving pricing accuracy by 15%. Conducted comparative analysis against stochastic volatility models (e.g., SABR) and applied methodologies to interest rate markets. Developed calibration algorithms and backtesting strategies in Python (Pandas, SciPy, NumPy).

Developed virtual sensors using deep learning models (CNN, LSTM), reducing prediction error by 20%. Optimized and deployed multi-target regression models on AWS using CI/CD pipelines with Git. Utilized PyTorch and TensorFlow frameworks for model training and inference.

Specialized master's program in quantitative finance covering derivative products, stochastic calculus, risk management, volatility modeling, and financial modeling. Focus on applying mathematical and computational methods to solve complex financial problems. Relevant coursework includes derivatives pricing, interest rate models, credit risk, and time series analysis.

Three-year engineering program specializing in Computer Science and Applied Mathematics. Focus on algorithms, software engineering, data structures, and numerical methods.