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 escal?

Experience working in a start-up environment / FinTech is an advantage.