Model Analyst – Wholesale Credit Risk

Description de la mission

Are you a skilled and experienced Model Analyst with a strong background in wholesale credit risk assessment? Do you possess a proven track record of building and validating critical credit risk models, including Probability of Default (PD), Exposure at Default (EAD), and Loss Given Default (LGD) models? If so, we have an exciting opportunity for you to join our team.
Position: Model Analyst
Location: London
Contract: 12 Months
About Us:
We are a leading financial institution known for our commitment to innovation, excellence, and responsible financial practices. As part of our dedicated Risk Management team, you will play a crucial role in enhancing our wholesale credit risk assessment capabilities.
Key Responsibilities:
Develop and refine Probability of Default (PD) models to assess the likelihood of borrowers or counterparties defaulting on financial obligations within a defined time frame.
Build Exposure at Default (EAD) models to estimate potential exposure a lender or institution could face in the event of default, aiding in risk mitigation strategies.
Construct Loss Given Default (LGD) models to quantify potential losses resulting from default, contributing to effective credit risk management.
Collaborate with cross-functional teams to ensure models align with business objectives and regulatory requirements.
Regularly report model outcomes for feedback and validation with the Prudential Regulation Authority (PRA), ensuring compliance with industry standards and regulations.
Participate in validation waves, closely monitoring the performance of the models and making necessary refinements.
Contribute to the continuous improvement of risk assessment methodologies and processes.
Requirements:
Bachelor’s or advanced degree in a quantitative discipline such as Statistics, Mathematics, Finance, Economics, or related field.
Proven experience in developing, validating, and implementing PD, EAD, and LGD models within the wholesale credit risk context.
Familiarity with regulatory bodies and their requirements, particularly the Prudential Regulation Authority (PRA) and its role within the financial ecosystem.
Strong analytical and problem-solving skills, with the ability to interpret complex data and translate it into actionable insights.
Proficiency in relevant programming languages and statistical software (e.g., Python, R, SAS).
Excellent communication skills, including the ability to present technical concepts to both technical and non-technical stakeholders.
Detail-oriented approach and a commitment to maintaining the highest standards of model accuracy and compliance.