Description

Houssem has over 1 year of experience as a Quantitative Analyst, specializing in risk metrics, predictive modeling, and statistical methods. Notably, Houssem developed and enhanced a Python-based monitoring framework for CCR/XVA risk metrics at HSBC and built predictive models to assess counterparty Probability of Default at Natixis CIB. Houssem also holds an Engineering degree from ENSTA Paris and an MSc from ENSAE.

Domaines d’expertise

Langues

Anglais
Bilingue ou natif
Français
Bilingue ou natif
Arabe
Bilingue ou natif

Préférences en matière de lieu de travail

Accepte de travailler sur site

Paris (jusqu’à 50 km)

HSBC
CCR/XVA Quantitative Analyst
mars 2025 - Aujourd'hui (1 an et 3 mois)
London, UK
•Developed monitoring and analytical tools in Python for quantitative risk analysis.
•Implemented numerical and modeling components in C++ for financial models.
•Contributed to the development of risk and pricing analytics frameworks.
•Worked on integrating quantitative tools within production data pipelines.
C++ Python Apache beam Jenkins SQL
Natixis CIB
Credit Quantitative Analyst intern
mai 2024 - décembre 2024 (7 mois)
Paris, France
• • Built predictive models to assess the Probability of Default (PD) of counterparties across multiple time horizons, leveraging advanced Machine Learning techniques such as Random Forests and Neural Networks.
• • Developed early warning signals to detect the potential degradation of counterparty credit quality, enabling proactive risk management.
• • Performed extensive data analysis and preprocessing, including PCA, data augmentation, imbalance treatment, data imputation and outlier detection, to ensure high-quality inputs for the models.
Machine learning Python Data science
ENS Paris-Saclay
Mathematics Researcher
mai 2023 - septembre 2023 (4 mois)
Paris, France
• • Conducted research on optimization and statistical methods for geospatial analysis, including ground movement monitoring and topographic mapping, and applied techniques such as linear optimization, graph-based models, and maximum likelihood estimation to solve the problem.
C++ Python Optimization Algorithmics