🎯 MSc Financial Engineering @ ESILV Paris | Seeking Quant Research/Systematic Trading Internship (Feb 2026)
💻 Python • C++ • C# • R • SQL | Advanced ML • Time-Series • Derivatives Pricing
Final-year Financial Engineering student specializing in Quantitative Research and Systematic Trading, with hands-on experience transforming legacy systems into production-grade trading infrastructure.
- TotalEnergies - Systematic & Algorithmic Trading Intern (May-Aug 2025)
- Spyros.AI - Python/ML Finance Intern (May 2024-Apr 2025)
- Trading Systems: Production infrastructure, real-time data processing (Numba, parallelization)
- Machine Learning: Ensemble models (CTA, FNN, LSTM), 25+ alpha factors, walk-forward validation
- Risk Management: Sharpe analysis, PnL distribution, statistical filtering, backtesting engines
- Financial Modeling: Derivatives pricing, stochastic calculus, time-series econometrics
C++ • Derivatives • Monte Carlo
Advanced options pricing models with simulation techniques
Python • Jupyter • Quantitative Research
Systematic commodity trading research and backtesting
Python • Portfolio Theory • Optimization
Optimized portfolio construction and risk management
Python • Scikit-learn • Feature Engineering
Classification models and ML pipeline implementations
- 🏭 Production Systems: Transformed legacy codebase to 40K+ line enterprise trading system
- ⚡ Performance: Achieved 100x code expansion with Numba JIT and parallel processing optimization
- 🤖 ML Innovation: Developed 25+ alpha factors with nested walk-forward cross-validation
- 📈 Trading: Deployed systematic FX/IR strategies with comprehensive risk-adjusted backtesting
- 🛡️ GenAI Security: Built secure LLM platforms for SQL querying without data exposure
- 📰 Media: Co-founded SquiidApe (100K+ subscribers in U.S. urban culture)
- 🎓 Completing MSc Financial Engineering at ESILV Paris
- 🔍 Seeking Quantitative Research/Systematic Trading internship opportunities
- 🏉 Playing competitive rugby (since 2008)
- 📚 Exploring advanced ML applications in systematic trading
📋 Technical Skills Deep Dive
- Python: Production systems, ML pipelines, backtesting frameworks
- C++: High-performance computing, derivatives pricing models
- R: Econometrics, statistical analysis, time-series modeling
- SQL: Database optimization, complex financial data queries
- C#/.NET: Enterprise applications and financial tools
- Derivatives: Pricing models, Greeks calculation, hedging strategies
- Risk Management: VaR, stress testing, portfolio optimization
- Time Series: ARIMA, GARCH, state-space models, regime detection
- Machine Learning: Feature engineering, ensemble methods, cross-validation
- ML: Scikit-learn, TensorFlow, Optuna, Numba
- Data: Pandas, NumPy, Milvus, Bloomberg Terminal
- Dev: Git, Linux, LaTeX, FastAPI, LangChain
- Finance: Quantlib, Bloomberg APIs, market data processing