Assistant Director, Wholesale Credit Risk Analytics

The core responsibility is performing model monitoring for Internal Ratings-Based (IRB) models across global wholesale portfolios. You must ensure all analytical outputs adhere to strict regulatory requirements, internal controls, and risk management standards, requiring a high degree of precision and audit-readiness.

You will act as a bridge between technical teams and business leaders. This requires translating complex quantitative findings into actionable business language for senior management while partnering with IT and Data Engineering to define and execute deployment roadmaps for analytical models.

You must demonstrate hands-on expertise in PD (Probability of Default), EAD (Exposure at Default), and LGD (Loss Given Default) modeling. The role requires applying statistical and machine learning techniques to solve complex business problems and drive significant value within the Wholesale Credit Risk function.

A significant challenge is the need to synthesize complex model outcomes into simple, business-oriented insights for senior management. Prepare by practicing how to explain the 'why' behind your model results and the business impact of your findings without relying on overly technical jargon.

While you are expected to use modern tools like LLMs and machine learning, you must operate within a highly regulated banking environment. The difficulty lies in justifying new methodologies to internal and external reviewers while maintaining strict compliance. Prepare by being ready to defend your choice of methods and demonstrate how you maintain documentation and governance standards.

Monitoring IRB models across diverse global wholesale portfolios involves navigating varying market conditions and data structures. Prepare by reviewing your experience in handling large-scale, multi-jurisdictional datasets and your ability to adapt analytical approaches to different economic contexts.

Review the regulatory requirements and best practices for Internal Ratings-Based (IRB) model monitoring. Focus on how to validate PD, EAD, and LGD models, including backtesting, stability testing, and discriminatory power assessment. This is critical for the role's core responsibility of monitoring wholesale portfolios.

Study the quantitative constructs of credit risk, specifically the definitions and drivers of Probability of Default (PD), Exposure at Default (EAD), and Loss Given Default (LGD). Understand how these metrics are used in capital quantification and risk exposure management within a banking environment.

Familiarize yourself with the lifecycle of model governance, including documentation standards, internal controls, and audit readiness. This is relevant to the role's requirement to adhere to regulatory standards and manage operational risks.

Practice building and monitoring predictive models using Python or SAS. Focus on implementing statistical tests for model performance and drift detection. This directly addresses the requirement for hands-on experience in solving complex business problems.

Gain practical experience in using Large Language Model tools for data synthesis or code assistance. Be prepared to explain how you would use these tools to improve analytical efficiency or interpret complex model outcomes for non-technical stakeholders.

Practice translating complex quantitative model results into simple, actionable business language for senior management. Prepare a presentation structure that highlights model performance, business impact, and risk implications clearly.