Final-year student at ENSAE Paris (Institut Polytechnique de Paris), specializing in Finance, Risks & Data. My academic and project work combine stochastic calculus, econometrics, and numerical methods with experience in derivative pricing, risk modeling, and quantitative/machine learning–based portfolio optimization.

Currently, I am conducting a research project at CMAP - École Polytechnique, developing a new stochastic model for integrated variance to generalize the Heston framework, using SPX and VIX data for volatility modeling, pricing, and calibration.

I am actively seeking a quantitative finance or structured products internship where I can apply rigorous mathematical frameworks and high-performance code to tackle real-world financial challenges.

Programming: Python, R, C++, SQL

Quantitative Finance: Derivative pricing & hedging, stochastic volatility modeling, Levy and GARCH processes, risk management, portfolio optimization

Machine Learning: Regression, time series forecasting, CNNs, non-linear prediction

Tools: Git, Jupyter, NumPy, SciPy, Pandas, scikit-learn, PyTorch, Matplotlib

NMF: Research project focusing on the implementation of NMF (Non-negative Matrix Factorization) and Deep NMF and the study of their implicit regularization (low rank bias).

CPP–Pricing-Using-EDP: Pricing tool for European options under the Black–Scholes PDE, designed for future extension to American and exotic derivatives with numerical schemes (FD, Monte Carlo).

PY–PriceTracker: Web scraping and data analysis tool to estimate market inflation dynamics based on real-time price data.

PY–Swap-Puzzle-Solver: Collaborative algorithmic project implementing BFS and A* search with a GUI to solve logical puzzles efficiently.

SAS–Profession-Impact-On-Happiness: Econometric analysis on how profession and partner’s occupation influence happiness levels, using regression modeling and statistical inference.

Email: malo.david@ensae.fr

LinkedIn: linkedin.com/in/malo-david