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Kaoutar User

@kaoutaruser

Quantitative Researcher focused on stochastic modeling and optimization to improve pricing, volatility modeling, and decision-making.

France

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What I'm looking for

I’m looking for a Quantitative Research role where I can apply stochastic modeling and optimization to volatility/derivative pricing, build reliable predictive models, and improve execution efficiency through rigorous benchmarking and fast numerical methods.

I’m a Quantitative Researcher specializing in stochastic calculus and model optimization, translating complex quantitative work into actionable insights for strategic decision-making.

In my internship at HSBC Europe Continentale, I optimized swaption volatility models, reconstructed implied volatility cubes, and benchmarked SABR against neural network and PCA approaches—while reducing arbitrage opportunities from 10% to 1%.

Earlier, I built deep learning pipelines for semantic segmentation, using Python and PyTorch to reduce inference time to 50ms per image with CUDA, achieving an mIoU of 70%. I also develop and evaluate stochastic models for derivative pricing using Monte Carlo, Euler discretization, and comparisons with closed-form results.

Experience

Work history, roles, and key accomplishments

Quantitative Researcher - Rates

HSBC Europe Continentale

May 2024 - Oct 2024 (5 months)

Optimized swaption volatility models and reconstructed implied volatility cubes to improve execution strategies and reduce arbitrage risks. Benchmarked SABR against neural network and PCA approaches to identify better modeling performance and outlier detection.

Anomaly Detection Volatility Swaption Modeling Implied Volatility Cubes Model Compression SABR Model Neural Networks Benchmarking Arbitrage Optimization

Quantitative Researcher - Rates (Front-Office) Internship - Volatility Modeling

HSBC Europe Continentale

May 2024 - Oct 2024 (5 months)

Optimized swaption volatility models to enhance market efficiency and reduce arbitrage risks. Reconstructed implied volatility cubes and benchmarked SABR model against neural networks and PCA, reducing arbitrage opportunities from 10% to 1%.

Stochastic Calculus Volatility Modelling SABR Neural Networks PCA Swaptions Implied Volatility Arbitrage Detection Python C++

Machine Learning Engineer

Diagnoly

Jun 2022 - Aug 2022 (2 months)

Developed and optimized a deep learning semantic segmentation model to automate image annotation. Implemented the model in Python with PyTorch/CUDA, reducing inference time to about 50 ms per image and improving detection of small critical anatomical structures.

Deep Learning Image Annotation Python PyTorch CUDA Inference Optimization Computer Vision Optimization

Machine Learning Engineer Internship

Diagnoly

Jun 2022 - Aug 2022 (2 months)

Developed and optimized a deep learning-based semantic segmentation model to automate image annotation processes. Reduced inference time from several minutes to 50 milliseconds per image via CUDA and achieved an mIoU of 70%.

Python PyTorch CUDA Semantic Segmentation Deep Learning Image Processing Model Optimization

Education

Degrees, certifications, and relevant coursework

Ecole des Mines de Saint-Étienne

Engineering Degree (MEng), Engineering

2020 - 2025

Activities and societies: Courses: Applied Mathematics, Statistical Learning, Probabilistic Models, Optimization Algorithms, Machine Learning, Computer Science, Information Systems, OOP, Computer Vision, Image & Pattern Recognition.

Engineering degree covering applied mathematics, statistical learning, probabilistic models, optimization, and computer science. Studied from September 2020 to February 2025.

École des Mines de Saint-Étienne

MEng, Engineering

2020 - 2025

Pursued an engineering degree with coursework in applied mathematics, statistical learning, and machine learning.

Sorbonne University

MMath, Probability and Finance

2022 - 2024

Pursued an M2 in Probability and Finance (EX DEA El Karoui) with coursework in stochastic calculus, quantitative finance, and numerical probability.

Sorbonne University and École Polytechnique

MMath (M2), Probability and Finance

2022 - 2024

Activities and societies: Courses: Stochastic Calculus & Mathematical Modeling, Numerical Probability, Quantitative Finance, Financial Derivatives, Stochastic Processes, Pricing, SDE discretization, Monte Carlo, Time Series.

MMath in Probability and Finance (M2) with coursework including stochastic calculus, numerical probability, quantitative finance, and stochastic processes. Completed the program between September 2022 and December 2024.

Lycée d’Excellence de Benguerir

Classe préparatoire aux Grandes Écoles, Preparatory Classes (Mathematics and Physics)

2018 - 2020

Activities and societies: Intensive two-year program for admission to Grandes Écoles, focused on mathematics and physics.

Two-year preparatory university program in mathematics and physics for entrance to top engineering schools. Attended from September 2018 to June 2020.