Credit Risk Quant

iason is an international firm that consults Financial Institutions on Risk Management.

iason is a leader in quantitative analysis and advanced risk methodology, offering a unique mix of know-how and expertise on the pricing of complex financial products and the management of financial, credit and liquidity risks.

In addition iason provides a suite of essential solutions to meet the fundamental needs of Financial Institutions .

We are looking for a Credit Risk Quant to join our team!

As a Credit Risk Quant you will be part of a high-profile iason team in the activity of model development and implementation within the Credit Risk Departments of an international financial institution.

Main Tasks

• Development and validation of Pillar I (PD, LGD, EAD) and Pillar II (Credit Portfolio model) Credit Risk models;
  
• Development of statistical models to analyze macro-economic variables and forecast time series (satellite model);
  
• Ad hoc quantitative analysis on credit risk issues
• Produce and maintain readable code in the main statistical software (Stata/ SAS);
  
• Data and Model Governance during the development and implementation phases;
  
• Support in the performance of Stress-Test exercises (EBA, ICAAP,...);
  
• Collaborate to the realization of Iason’s development projects and research.

Analytical and Technical Requirements

• MSc in Economics, Mathematics, Physics, Mathematical Engineering, Statistics or other quantitative courses;
  
• Good math and statistics foundations, especially in econometrics, macroeconomics and basic calculus;
  
• Programming skills: Knowledge of at least one of the following: SAS, Stata, Python, R;
  
• At least 6 months of experience in development of Credit Risk models (Pillar I, Pillar II and/or IFRS9);
  
• Basic knowledgeof the regulatory framework for credit risk (CRR, EBA guidelines on PD/LGD, ECB guide to internal models, EBA RTS and IFRS9 framework);
  
• Fluent English;
  
• Be proactive and very detail-oriented;
  
• Analytical mindset and well-organized approach to problem-solving.