• 🏢 20y-Exp Quant/Quant Dev | Trading, Research & Execution in TradFi-DeFi
• 💫 PhD | [Mathematical Finance] | Paris Sorbonne, France
• 🌟 MSc Economics | Paris Sorbonne, France

Seasoned financial engineer and product innovator with extensive experience in capital markets and banking. I specialize in agent-based and machine learning applications, with a focus on crypto markets and decentralized finance (DeFi) where I have been operating for the last 3 years.

My expertise includes designing and implementing ModelOps platforms, quantitative modeling, trading execution system and leading projects in Model risk management, pricing, and regulatory compliance. As an entrepreneur and R&D leader, I excel in building new business opportunities, managing relationships, and driving innovation. I have a broad network in DeFi, FinTech, and Academia.

Known for my collaborative approach and ability to thrive in challenging environments, I'm a self-starter, early adopter, and effective communicator. I enjoy exploring new ideas, engaging in debates, and continually learning from others.

• Focus on LOB dynamics, market impact models, and TCA.

• CPO-ed, an AI agent-based platform destined to Quant Dev users to import, build, test, deploy and monitor autonomous agents running on the blockchain (EVM mostly) to execute low to mid-frequency DeFi strategies relying on AI-ML models in a non-custodial way. Led the Quant and Product teams for 2y, 0-to-1, product-market fit and go-to-market
• Collaborated with Igor Halperin's team and Professor Petter Kolm.
• Designed an Optimal Order Execution system using RL and MLOps design patterns, aimed at building a trading-as-a-service platform for institutional clients (details available on request).
• PO-ed and GTM-ed a ModelOps platform, cloud and open-source based for MLC operationalization for TradFi Institutional clients.

• Founded an algo trading tech and research firm.
• Built an aggregated order book for Europe's main MTFs, simulating up to 1 million orders/second.
• Analyzed MiFID best execution principles in high-frequency markets using C++ and Java for over 50 different order types. (repo provides a glimpse).

• Developed systems in Java to study HFT systemic impacts for regulation purposes.
• You can ask me for an [executive deck] presenting the PoC-stage project.

• Modeling the quantum-chaotic nature of microstructure to refine limit order patterns and maximize MM rebates (work in progress).
• Building a na agent-based and RL-based backtesting engine for market-making strategies on digital assets.

Python	Rust	C++	MATLAB	Git	Jupyter	Linux
Bash	SQL	TheGraph			PyTorch	Kubernetes