Credit Risk Analytics Manager

Position Overview

Our client, a premier international banking group, is seeking an ambitious Assistant Credit Risk Analytics Manager to join their Regional Risk Analytics team in Asia Pacific.

Working directly alongside the Regional Head of Wholesale Analytics, you will contribute to the end-to-end development, validation, and performance monitoring of wholesale credit risk models (PD, LGD, EAD). This role provides broad regional exposure across corporate banking lines and key risk frameworks, including IFRS 9, Stress Testing, Economic Capital, and Climate Risk, ensuring local alignment with global risk governance and regulatory standards (HKMA / Basel).

Key Responsibilities

• Wholesale Model Development & Validation: Assist in designing, calibrating, and validating wholesale credit risk models (PD, LGD, EAD) across diverse commercial and corporate banking product portfolios.

• Risk Analytics & Regulatory Frameworks: Deliver quantitative modeling support across IFRS 9 expected credit loss, stress testing, economic capital, and climate risk assessments.

• Policy Integration & Governance: Review and refine wholesale credit policies, guidelines, and risk measurement tools from a modeling perspective, ensuring compliance with HKMA and Basel capital adequacy rules.

• Stakeholder & Regulatory Liaison: Collaborate with frontline business units, product teams, group risk leaders, and regulatory bodies on risk rating systems, credit proposals, and audit remediation.

• Automation & Generative AI Innovation: Utilize programming tools and GenAI applications to streamline routine analytical workflows, model documentation, and dashboard reporting.

Candidate Requirements

• Education: Bachelor’s degree or above in Statistics, Quantitative Finance, Risk Management, Mathematics, or a related quantitative field.

• Experience: Minimum 2 to 5 years of solid experience in credit risk modeling, risk analytics, or quantitative risk management within wholesale or corporate banking.

• Technical & Coding Proficiency:

  • Hands-on proficiency in programming languages such as Python, SAS, R, or Matlab.

  • Exposure to modern analytics delivery tech stacks (e.g., GCP, VS Code, GitHub) and dashboarding tools.

  • Practical experience or strong interest in applying Machine Learning and Generative AI tools to automate analytical tasks.

• Regulatory Knowledge: Sound understanding of wholesale credit risk parameters (PD/EAD/LGD), Basel capital requirements, HKMA guidelines, and IFRS 9 frameworks.

• Soft Skills: Strong communication, report-writing, and stakeholder management skills; resilient and comfortable delivering under tight regulatory timelines in a fast-paced environment.