About Antoine
I'm a quantitative researcher and machine learning engineer with 3+ years of experience building production-grade models and data systems across top financial institutions — Société Générale CIB (New York), Axa Investment Managers, Premialab (London), LCH, and an algo-trading firm in Tel Aviv.
My work sits at the intersection of advanced ML, statistical modeling, and large-scale data engineering. I've designed ML-driven alpha signals for cross-asset portfolios, built generative AI models (GANs) for synthetic time series, developed predictive models for fund holdings, and shipped high-performance backtesting pipelines processing hundreds of thousands of data points daily. Every project I deliver is modular, well-documented, and built to scale.
What I bring to the table:
— Machine Learning & AI — Supervised/unsupervised models, feature engineering, signal extraction, GANs for synthetic data generation
— Python Engineering — 8 years of Python. Clean, production-ready code. Multi-processing, modular architecture, API integration.
— Data Pipelines & Engineering — End-to-end frameworks: collection, cleaning, aggregation, statistical analysis. SQL, AWS, large-scale data (Bloomberg, Refinitiv, MSCI)
— Statistical Modeling — PCA, time series analysis, Nelson-Siegel, econometrics, stochastic calculus, hypothesis testing at scale
— Quantitative Finance — Systematic strategies (options, cross-asset), risk modeling, backtesting engines, P&L attribution
French
Native or bilingual
English
Native or bilingual
Can work on-site
Paris (up to 50km)
Experience
- Société GénéraleQIS Structuring (Quantitative Investment Strategies)BANKING AND INSURANCEAugust 2024 - January 2026 (1 year and 5 months)New York, United States• Designed and implemented systematic option strategies (Tail Hedges, Short Volatility, Dispersion) for hedge-fund and institutional clients• Developed alpha signals with ML to construct cross-asset baskets of strategies, estimate asset betas and systematise overlays / rebalances• Partnered with trading desks for intraday signal validation, daily P&L explain, and computing strategy risk exposures• Developed agile intraday backtesting libraries (Python, Multi-Processing, SQL, Bloomberg) → +5× time efficiency, for structuring and trading
- PremialabQuant Researcher InternBANKING AND INSURANCEJune 2023 - December 2023 (6 months)Londres, United Kingdom• Built a framework testing the predictive power of Carry and term structure (PCA/Nelson-Siegel) for Fixed Income, Equities, Options and QIS• Proposed a new approach to quantify Carry factor for daily-reallocating systematic strategies using approximations for CTD and Duration• Built end-to-end framework at QIS (strategy) level, from data collection, cleaning, aggregation to statistical analysis. Framework includes IRSpricing, data approximation, factor exposure estimation and data tools (Refinitiv, AWS, DBeaver, MSCI)
- LCHQuant Researcher InternBANKING AND INSURANCEJanuary 2022 - June 2022 (5 months)Paris, France• Implemented a Gen AI model : Time series Generative Adversarial Network (TGANs 2019 ) to generate synthetic time series of returns• Developed different metrics, aligned with literature, to assess the relevance and performance of the model for Quant needs
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Education
- Scientific Preparatory classes to attend French engineering "Grandes écoles"Lycée Condorcet Paris2019Scientific Preparatory classes to attend French engineering "Grandes écoles"
- IngénieurENSAE ParisENSAE Paris is one of the 10 most prestigious French Grandes Ecoles of engineering. ENSAE Paris is known as the branch school of École Polytechnique for statistics, data science, machine learning and economics. It is one of France's top schools of economics and statistics and is directly attached to the French Ministry of Economy and Finance. Relevant courses: - Applied mathematics : Statistics, Functional and Convex Analysis, Optimization, Theory of Machine Learning, Stochastic Calculus - Computer Science :Data analysis in Python, SAS, STATA, R - Econometrics ( STATA ) - Finance - Economics