Hedi K.

Polytechnic engineer with 4+ years of Python and 2+ years of applied ML research. Hands-on experience in algorithmic trading systems, asset pricers, and risk management. I turn mathematical rigor and statistical insights into production-ready results. - Forex hedging: ML forecasting for FX markets that cut options hedging costs by 3.5% (BIAT). - Algorithmic Trading: Co-developed 3 out-of-sample strategies with ~190% cumulative net return over 4 years (net of costs/slippage), robust under stress tests. - Derivatives pricing: Coded numerical methods for energy options contracts with full sensitivity analysis, benchmarking speed, accuracy, and consistency. **Focus Areas (what I deliver)** - Coding & performance: efficient Python, parallelization, scalable/cloud pipelines (GCP), speed optimization - Data analysis: data extraction, cleaning, preprocessing, exploratory analysis, statistical modeling, pattern detection, decision-ready insights - Predictive modeling: time-series and tabular ML development, feature engineering, hyperparameter optimization, robust validation, testing - Derivatives pricing & hedging: mathematical foundations and numerical methods (Finite Elements, Lewis Fourier, Monte Carlo) - Systematic trading: signal generation, position sizing and risk management, backtesting, paper trading and deployment of algorithmic strategies **Technical Stack:** Python, NumPy, Pandas, TensorFlow, PyTorch, Scikit-learn, Ray, joblib, Git, GCP, SQL, Excel, Matplotlib, C/C++, SLURM (HPC) With a multidisciplinary foundation across mathematics, engineering, computer science, economics, and finance, I'm open to applying ML to diverse domains—including emerging areas like GenAI and LLMs. If you need a partner who can design, code, validate, and ship trading algorithms or ML solutions with real-world impact, let's talk.