Model Risk Officer

Job Description

• Provides objective, independent, and effective challenge over the design, development, implementation, use, monitoring, and performance of models across the Bank, as part of the second line of defense.
• Supports the maintenance of a complete and accurate enterprise model inventory, and independently reviews model ownership, intended use, materiality, risk classification, and validation frequency.
• Develops and maintains a risk-based validation plan that prioritizes models by materiality, complexity, customer impact, regulatory significance, and financial exposure.
• Leads independent validation of models across all business units of the bank covering conceptual soundness, data quality, performance testing, implementation verification, and controls.
• Issues independent validation of opinions and recommendations and monitors remediation progress through closure.
• Escalates material, overdue, or unresolved model-risk concerns to the Chief Risk Officer and relevant governance committees.
• Prepares periodic model-risk reporting — inventory status, validation coverage, findings, and remediation progress — for governance committees, the Risk Oversight Committee, and the Board.
• Supports regulatory examinations, Internal Audit, and External Audit engagements involving model risk, validation, and governance.
• Contributes to the enhancement of the Model Risk Management Policy, validation standards, model tiering methodology, and related tools and templates.
• Provides guidance and training to model owners, developers, and other stakeholders on model-risk requirements and validation expectations.

Qualifications

• Bachelor's degree in Statistics, Mathematics, Economics, Data Science, Computer Science, Engineering, Actuarial Science, Finance, Accounting, or another relevant quantitative discipline; a postgraduate degree in quantitative, financial, technology, or risk-management discipline is an advantage.
• At least 5 years of relevant experience in banking, financial services, fintech, model validation, model risk management, quantitative analytics, data science, or audit of quantitative models.
• Demonstrated experience independently reviewing or challenging statistical, financial, risk, or machine learning models; experience in a digital bank or highly automated financial institution is preferred.
• Strong knowledge of statistical modeling, regression, classification, forecasting, time-series analysis, and machine learning, along with model validation techniques such as back testing, benchmarking, calibration, and stability/sensitivity testing.
• Working knowledge of banking risk disciplines, including credit risk, expected credit loss, capital, liquidity, market risk, operational risk, fraud, and AML/CFT and others.
• Proficiency in SQL and at least one analytical programming language or platform such as Python, R, SAS, or MATLAB; ability to review model code, data transformations, and documentation.
• Excellent written and verbal communication skills, with the ability to present technical findings clearly to senior management, governance committees, auditors, and regulators.
• Independence, integrity, and professional skepticism, with the judgment and willingness to raise and escalate material concerns.
• Experience working in a start-up environment / FinTech is an advantage.