Marc Partensky

 

Data Science Engineer · Market Finance · Quantitative Methods

M2 Market Finance @ CNAM · MEng Data Science @ ISEP · ex-BNP Paribas

 

 

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I'm an engineer at the intersection of quantitative finance and software engineering — I build the kind of systems that sit between a mathematical model and a production environment.

 

My background spans data engineering at BNP Paribas, a Data Science engineering degree (ISEP), and an ongoing Master 2 in Market Finance (CNAM) covering risk modeling, derivatives, and portfolio theory. I like things that are fast, correct, and well-understood.

 

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Quantitative Skills

 

Finance: Market Risk Modeling · Value at Risk (VaR) · Fixed Income · Options & Futures · Portfolio Management · Monte Carlo Methods · Financial Macroeconomics

 

Mathematics: Probability Theory · Linear Time Series · Statistics · Optimization · Linear Algebra · Machine Learning

 

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Tech Stack

 

 
Python  ·  C#  ·  SQL  ·  JavaScript / React  ·  Bash  ·  Nix
 
Pandas  ·  NumPy  ·  SciPy  ·  PyTorch  ·  Streamlit
 
PostgreSQL  ·  MongoDB  ·  AWS  ·  Docker  ·  Kubernetes
 
Tableau  ·  PowerBI  ·  Dataiku
 

 

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Experience

 

BNP Paribas – ITGP · Data Analyst & Infrastructure (apprenticeship, 2022–2023)

• Built ETL pipelines and internal reporting dashboards (Python, Tableau, Dataiku, PostgreSQL)

• Deployed a full-stack app on IBM Cloud (Node.js + PostgreSQL) under IT governance constraints

 

Pickup Services / La Poste · Fullstack Developer (apprenticeship, 2020–2022)

• Developed ETL tools for dynamic i18n in a React app via MongoDB and C# (ASP.NET Core)

• Worked Scrum, shipped new features on a production-grade parcel simulation system

 

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Selected Projects

 

| 📐 fourier | Fourier series decomposition & epicycloid visualization — spectral analysis applied to arbitrary 2D signals |

| ⚡ python-games | Numerical simulation engine built from scratch: physics, particles, chaos equations, Mandelbrot, solar system dynamics |

| ❄️ nixos | Fully declarative, reproducible NixOS environment — deterministic dep management for data science workflows |

| 🐳 docker | Self-hosted infra configs (Kubernetes, Docker Compose) |

| ⚙️ nvim | Neovim config |

| 🌐 website | Personal website |

 

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🔨 Currently building

 

• option-pricer — Black-Scholes closed-form + Monte Carlo simulation in Python, with convergence analysis

• backtest — Event-driven backtesting engine on historical market data

• portfolio-optimizer — Markowitz mean-variance optimization with efficient frontier visualization

 

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Achievements & Certs

 

🏆 Winner – Greencode Hackathon (Dassault Outscale, 2022)

🥈 2nd Place – FRHack (ANFR, 2022)

📜 AWS Cloud Practitioner · Deep Learning Specialization (Coursera)

🗣️ English: fluent (TOEIC 930 · TOEFL 92)

 

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_{❄️ nixos, btw}