Erreiche deine beruflichen Ziele als Senior Quantitative Analyst bei Quanteam in Frankfurt am Main

Seeking highly skilled and experienced Credit Risk Modeller to join our dynamic team. The ideal candidate will have a deep understanding of Internal Ratings-Based (IRB) models, specifically Probability of Default (PD) and Loss Given Default (LGD) models. As a Credit Risk Modeller, you will play a pivotal role in developing, validating, and maintaining these critical credit risk models to support our organization's risk management and regulatory compliance efforts.

Key Responsibilities:

• Conduct regular statistical analyses such as risk analytics, default risk modelling and other similar tasks.
• Communicate the results of analyses clearly, including to senior management, in a way which adds significant value to business decisions.
• Design and implement the calibration and backtesting methodologies and support the Risk Systems teams responsible for the corresponding production processes
• Produce documentation to ensure replication of results and fulfil governance requirements
• Review existing strategies and practices, understand business problems and working with colleagues to scope out and deliver relevant analysis and modelling through to implementation.
• Develop within the team’s common Python code base.
• Assess the theoretical assumptions of models, ensuring they are fit for purpose

Requirements:

• Fluent German
• Good understanding of quantitative finance and related risk issues including derivative principles, option valuation, probability and statistics; coupled with a strong practical, and business focused, approach to modelling, valuation and risk management
• Experience from IRB (PD, LGD) models
• Strong communication skills
• Educational background within a highly quantitative subject (mathematics, quantitative finance, operational research, science, engineering or similar)
• Attentive to detail, striving to achieve deep understanding and top of class implementation.
• Experience of code development (including Python)
• Strong knowledge of risk management best practice.