About
About Brownian Bridge
A quantitative finance learning platform built around observable skill — not self-reported competencies.
Founder
Benoit Vandevelde
Benoit Vandevelde has spent more than 15 years building and validating pricing models across FX, Equity, Fixed Income, and Commodities at institutions including Deutsche Bank, Barclays, BNP Paribas and ICBC Standard Bank. He currently works as Director, Quantitative Modelling at Dymon Asia Capital in Singapore.
He has lectured in financial mathematics and C++ applied to derivatives pricing for more than 10 years at Master's level at Université Gustave Eiffel in Paris — implementing research by Heston, Dupire, Andersen, and others in production-grade C++.
LinkedIn profile →Career
Mar 2025 – present
Director, Quantitative Modelling
Dymon Asia Capital, Singapore
Sept 2023 – Mar 2025
Director, Cross-Asset Model Validation
ICBC Standard Bank, London
Sept 2019 – Aug 2023
Team Lead, Commodities Model Validation
Deutsche Bank, London
May 2017 – Aug 2019
VP, Front Office Quantitative Analyst, CCR
Barclays, London
Jul 2016 – Apr 2017
Quantitative Analyst, R&D
Numerix, London
2014 – 2016
Quantitative Analyst, Pricing & Model Validation
Lloyds Bank, London
2013 – 2014
Quantitative Analyst, Front Office
BNP Paribas, London
Jan 2016 – present
Lecturer, Financial Mathematics & C++ for Derivatives Pricing
Université Gustave Eiffel, Paris — Master's level (parallel to industry roles)
Education
Master 2, Mathematics and Applications in Finance — Ecole Nationale des Ponts et Chaussées
Engineering diploma (Finance & CS majors) — Ecole Centrale de Nantes
Why this platform exists
Two careers, running in parallel for nearly a decade. During the week: building and validating pricing libraries at banks and a hedge fund. In term time: lecturing financial mathematics and C++ to Master's students in Paris.
The gap between those two worlds was always visible. Students who understood the theory struggled to translate it into code their interviewers could trust. Candidates who could quote a model often could not price an instrument on a desk, handle a numerical failure, or explain a calibration decision under pressure.
The CV, the grade, and the institution name were doing most of the signalling — and doing it poorly. Brownian Bridge is built to close that gap: a platform where technical ability is made visible through working implementations, verified reasoning, and observable output.
The platform
Brownian Bridge provides structured learning in mathematical finance — stochastic calculus, derivatives pricing, numerical methods, calibration, and risk — through rigorous courses, interactive labs, C++ and Python implementations, and coding challenges. Work completed on the platform is inspectable. Skill earned here is observable.
Content standards
Assumptions stated before derivation.
No model appears without its mathematical and market context made explicit.
Code that compiles and runs.
With unit tests. Pseudocode is labelled as such.
Limitations and failure modes discussed.
A model without known failure modes described is incomplete content.
Numerical results reproducible.
With stated seeds, tolerances, and environment specifications.
Conventions always made explicit.
Compounding basis, day count, volatility units — specified, not assumed.
For employers
Employers who use the platform can inspect candidate work directly — not summaries, not self-reported skill levels. The data surfaced is derived from observable behaviour: problems solved, code submitted, peer reviews conducted, challenge rankings achieved. Brownian Bridge does not certify candidates. It makes their technical work inspectable to hiring teams who know what to look for.
Hiring signal overviewBrownian Bridge — independent platform, founded 2025.