Ilyes kerkeni

👋 About

Linkedin, E-mail, Phone

Hello, I’m Ilyes — a quantitative researcher and graduate of ENSTA Paris and the Master’s program in Probability and Finance (formerly DEA El Karoui). I specialize in applying advanced mathematics, machine learning, and stochastic modeling to solve real-world problems in financial markets. Previously, I worked as a Quant Strategist on the FX team at Goldman Sachs in London, where I developed a time-dependent Heston model to improve the pricing of exotic derivatives. I also gained experience as a quantitative research intern working on structured products and volatility derivatives, and as a quant-data scientist applying statistical techniques to analyze complex market data. Today, I collaborate with clients globally on projects ranging from arbitrage-free volatility surface construction to reinforcement learning-based trading strategies. I’m passionate about turning academic research into practical, high-impact solutions for trading and risk management. Driven by curiosity and a commitment to continuous learning, I’m always exploring new ideas.

💼 Professional Experience

Quantitative Researcher
Self-employed under micro entreprise

Paris, France
November 2024 - Now

  • Working independently with clients across the globe on quantitative finance and algorithmic trading projects.
  • Project 1: Implied Volatility Surface Construction.
  • Project 2: Machine Learning for Equity Trading.

Quantitative Strategist
Goldman Sachs

London, UK
May 2024 - October 2024

  • FX desk strats
  • Developed advanced quantitative models to enhance pricing accuracy for FX derivatives and exotic options, including barrier options, forward-start options, volatility swaps, and variance swaps. Collaborated directly with traders and strategists to design and implement solutions aligned with trading requirements.

Quantitative trader (full remote)
Eruditis

Milwaukee, USA
Juin 2022 - April 2024

  • Part Time job (Freelance)
  • Exploring and implementing trading strategies using Machine Learning and reinforcement Learning.
  • Implementing research papers.

Quant-Data scientist
Crédit Agricole CIB

Paris, France
01 March 2023 - 31 August 2023

  • Project for USD VOL DESK
  • Working side to side with the IRD exotic trader and the head of AI GMD department to make financial market predictions and modeling
  • Collecting and analyzing data from US insurers, forward rates, implied volatilities, and index values to anticipate and explain insurers’ trading activities.
  • Building and maintaining new models, gaining 20points in performance.

Quantitative Researcher
HSBC Paris

Paris, France
29 August 2022 - 28 February 2023

  • Research Project under the aiges of “Europlace Institut of Finance”
  • Working with both machine learning models and multivariate rough volatility models, gaining expertise in the randomized signatures (rSig) pricing model and the quadratic Gaussian (qGauss) models.
  • Developing rich models that remain numerically tractable for the fast generation of autocallable prices and sensitivities.

Quantitative Reasearcher
Crest, Ensae Paris

Paris, France
1 Juin 2022 - 19 August 2022

  • Exploring several approaches to estimate VaR and CoVaR using Parametric Models, Non-parametric models, Semi-parametric models
  • Garch models, Monte carlo simulation, resampling, bootstrapping, python/R programming

🎓 Education

M.S. “Probabilitées et finance ex DEA EL KAROUI”
Ecole Polytechnique - Sorbonne University

Paris, France
September 2023 - 2024

  • Courses: Machine Learning-Neural Networks-Deep Learning, Stochastic control and optimisation, High-frequency finance: probabilistic tools, statistical modeling, Machine learning and optimal trading etc..
  • GPA=4

Enginnering degree
Ensta Paris

Paris, France
September 2020 - 2024

  • GPA=4
  • Applied Mathematics (Statistics, Probability, Markov chains, Stochastic calculus, Optimization, Martingales, Time series, Monte-Carlo Methods, Machine learning, mathematical models in finance).

🏆 Projects

  • Team enginner project H1N1 & flu predictions The aim is to predict whether a person is vaccinated against H1N1 and seasonal flu. we used here vast majority of classification mdeols (tree based models: XGboost, Catboost, RandomForest) neural networks ect..
  • Predicting rent Prices
  • Image Classififcation deploying a convolutional neural networks (with C) capable of classifying images according to their categories.
  • Algorithmic Trading on Stocks (via IBKR API) and on Cryptos (via Binance API) The aim here is to build statistical arbitrage strategies on different markets, implement them in Python, and apply them to Interactives Brokers.

💾 Resume

🎖️ Certifications

💻 Technical skills

  • Python : Pandas, numpy, Sikit-Learn, Pytorch, Tensorflow, Keras
  • C++, C
  • Matlab
  • R
  • Excel, PowerPoint, LaTex
  • Dataiku, IBM waston studio

🎳 Hobbies

  • Reading 📚

  • GYM 🏋🏻‍♀️ and Swimming 🏊🏻‍♂️ (Almost 5 days per week)

  • Tennis 🎾 (Rafa Nadal)

  • (Ex) professional basketball player 🏀 .

  • Traveler 🧳.