Haiub A.

Developed and automated credit risk control frameworks using SQL, Python and Power BI to monitor counterparty exposures, ratings migrations, probability of default (PD) and regulatory compliance across investment funds and mandates.

• Designed and maintained custom Power BI dashboards tracking key credit risk indicators (ratings, PD/LGD, sector/country concentration, limit utilisation) enabling faster, data-driven decision making by senior management.

• Optimised SQL queries and automated daily/monthly credit risk reporting processes, significantly increasing efficiency and accuracy of regulatory and internal control tasks (UCITS, CSSF/FINMA).

• Collaborated with cross-functional teams (Front Office, Compliance, Reporting) to ensure proper application of credit risk methodologies, exposure limits, concentration thresholds and counterparty risk controls.

• Developed and validated machine learning models to enhance credit risk forecasting and predictive analysis of portfolio performance, default probabilities and ratings migration scenarios.

Built credit-risk scoring and market-risk models (PD, LGD, EAD, VaR, Expected Shortfall, stress testing) for counterparties and portfolios.

• Performed data extraction and analysis of financed companies to assess creditworthiness, exposures and market-risk factors, ensuring compliance with Basel III/IV and UCITS limits.

• Developed Python/SQL automated pipelines for PD, LGD and VaR calculations, improving data quality and reducing manual effort.

• Designed Power BI dashboards showing scoring results, market sensitivities, limit utilisation and stress-testing scenarios for senior management and investment committees.

• Produced regulatory-style risk reports and documented methodologies for audits and internal model governance.

• Collaborated with Front, Middle Office and Compliance to validate model outputs, discuss risk concentrations, develop early-warning indicators and propose mitigation strategies.

• Conducted back-testing and validation of credit and market-risk models to ensure accuracy, robustness and regulatory alignment.

Conducted data-driven investment research and risk assessment, analysing macroeconomic trends, sector dynamics and company fundamentals to identify opportunities and potential risk factors for portfolio strategies.

• Supported portfolio managers with quantitative analysis of fund allocation, portfolio optimisation, performance attribution and investment risk metrics (volatility, tracking error, VaR) to ensure compliance with investment guidelines.

• Performed scenario and stress testing on portfolios to evaluate sensitivity to market movements, interest rates, credit spreads and sector exposures.

• Created tailored risk/return reports and interactive visualisations in Power BI/Tableau for client meetings, investment committees and international roadshows, highlighting key risks and performance drivers.