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Financial and AI engineer with a Master’s in Financial Engineering, focused on ML/DL credit risk modeling, financial time-series forecasting, and LLM-driven financial analytics. Experienced in building robust risk and forecasting systems that perform reliably under market regime shifts. Skilled in unsupervised regime detection, ensemble modeling, bias/leakage prevention with purged/embargoed validation, and transforming time-series data for improved signal detection. Passionate about turning quantitative research into production-ready solutions for trading, investment, and financial decision-making.…Financial and AI engineer with a Master’s in Financial Engineering, focused on ML/DL credit risk modeling, financial time-series forecasting, and LLM-driven financial analytics. Experienced in building robust risk and forecasting systems that perform reliably under market regime shifts. Skilled in unsupervised regime detection, ensemble modeling, bias/leakage prevention with purged/embargoed validation, and transforming time-series data for improved signal detection. Passionate about turning quantitative research into production-ready solutions for trading, investment, and financial decision-making.

Ahmed Gomaa

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Financial and AI engineer with a Master’s in Financial Engineering, focused on ML/DL credit risk modeling, financial time-series forecasting, and LLM-driven financial analytics. Experienced in building robust risk and forecasting systems that perform reliably under market regime shifts. Skilled in unsupervised regime detection, ensemble modeling, bias/leakage prevention with purged/embargoed validation, and transforming time-series data for improved signal detection. Passionate about turning quantitative research into production-ready solutions for trading, investment, and financial decision-making.…Financial and AI engineer with a Master’s in Financial Engineering, focused on ML/DL credit risk modeling, financial time-series forecasting, and LLM-driven financial analytics. Experienced in building robust risk and forecasting systems that perform reliably under market regime shifts. Skilled in unsupervised regime detection, ensemble modeling, bias/leakage prevention with purged/embargoed validation, and transforming time-series data for improved signal detection. Passionate about turning quantitative research into production-ready solutions for trading, investment, and financial decision-making.

Available to hire

Financial and AI engineer with a Master’s in Financial Engineering, focused on ML/DL credit risk modeling, financial time-series forecasting, and LLM-driven financial analytics. Experienced in building robust risk and forecasting systems that perform reliably under market regime shifts.

Skilled in unsupervised regime detection, ensemble modeling, bias/leakage prevention with purged/embargoed validation, and transforming time-series data for improved signal detection. Passionate about turning quantitative research into production-ready solutions for trading, investment, and financial decision-making.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

Arabic

Fluent

English

Advanced

Work Experience

Quant & Financial Engineer (MSc in Financial Engineer)

January 1, 2024 - Present

Led quantitative research in geopolitical risk stress testing by designing a Bayesian Belief Network to model probabilistic shocks on Brent Crude for improved what-if simulations. Performed strategic factor attribution using Fama-French 5-factor analysis with robust regression to separate true manager skill from expensive factor exposure and support capital reallocation decisions.

AI Engineer at Hyper Solutions

January 1, 2024 - Present

Built an unsupervised HMM-based market regime detection pipeline with automated “Risk-Off” triggers, reducing maximum drawdown by 18% during volatility shifts. Developed a Multi-Output LSTM architecture for multi-asset alpha generation to improve cross-ETF correlation tracking while reducing computational cost. Implemented a heterogeneous stacking ensemble to increase out-of-sample reliability by 12% and smooth the equity curve during market spikes.

Data Scientist (Remote Internship) at UN - Data Scientist

March 1, 2023 - March 1, 2024

Performed model integrity and bias audits by building a purged/embargoed cross-validation system to detect and remove data leakage that caused “phantom profits” in test environments. Implemented time-series-to-image conversion using Gramian Angular Fields (GAF) to support CNN-based visual signal detection, improving pattern recognition vs. standard indicators. Applied denoising using Marčenko–Pastur Law to extract stable eigenvalues and reduce overfitting by focusing models on underlying drivers rather than statistical noise.