We're Hiring: Senior Model Risk Specialist
Location: United Arab Emirates (Remote)
Employment Type: Full-Time
Experience Level: Senior
Work Arrangement: Fully Remote
About UsWe are a globally focused organization committed to strengthening risk management, financial resilience, governance, and data-driven decision-making across diverse markets.
Our Risk, Finance, Compliance, Technology, Data, and Business teams collaborate to ensure quantitative models are appropriately developed, governed, validated, monitored, documented, and used within clearly defined risk and control frameworks.
The RoleWe are seeking an experienced Senior Model Risk Specialist to lead model risk management activities covering model governance, model validation, model inventory, model performance monitoring, model limitations, remediation, and risk assessment.
The ideal candidate will combine strong quantitative and analytical capabilities with a deep understanding of model risk management principles, regulatory expectations, model governance, and financial risk. The role will provide independent challenge over models used for credit risk, market risk, liquidity risk, capital, forecasting, stress testing, pricing, fraud, financial crime, and other business applications.
Key Responsibilities
- * Develop, implement, and maintain model risk management frameworks, policies, standards, procedures, and governance processes.
- Maintain a comprehensive enterprise-wide model risk management framework aligned with organizational risk appetite and regulatory expectations.
- Establish model classification, risk-tiering, materiality, and criticality methodologies.
- Maintain and oversee the enterprise model inventory and model lifecycle records.
- Ensure all material models are appropriately identified, registered, classified, documented, approved, and monitored.
- Establish requirements for model development, implementation, validation, approval, monitoring, change management, and retirement.
- Provide independent challenge over model development methodologies, assumptions, data, methodologies, limitations, and intended uses.
- Conduct or oversee independent model validation and model risk assessments.
- Review model conceptual soundness, theoretical foundations, methodology, assumptions, and limitations.
- Assess the appropriateness of model inputs, variables, parameters, data sources, and transformation methodologies.
- Evaluate model development samples, testing approaches, statistical methodologies, and performance metrics.
- Review model implementation to ensure production systems accurately reflect approved model specifications.
- Assess model outcomes for accuracy, stability, consistency, and fitness for intended purpose.
- Perform or oversee quantitative validation techniques including back-testing, benchmarking, sensitivity analysis, stress testing, challenger models, and outcome analysis.
- Evaluate model performance using appropriate statistical and business metrics.
- Assess model discrimination, calibration, stability, predictive accuracy, and robustness where applicable.
- Review model assumptions and assess the impact of material assumptions on model outcomes.
- Identify model weaknesses, limitations, uncertainties, and potential sources of model risk.
- Assess data quality, representativeness, completeness, consistency, and suitability for model purposes.
- Review data lineage and controls supporting material model inputs.
- Assess model performance across relevant portfolios, customer segments, products, geographies, and economic conditions.
- Monitor model performance and identify deterioration, instability, drift, or emerging model risks.
- Establish model monitoring thresholds, triggers, escalation criteria, and review frequencies.
- Coordinate periodic model reviews and ongoing monitoring activities.
- Assess model changes and determine whether changes require revalidation, approval, or additional testing.
- Review model redevelopment, recalibration, enhancement, and methodology changes.
- Ensure appropriate governance over temporary models, expert judgment overlays, overrides, and compensating controls.
- Assess the use of alternative methodologies, manual adjustments, and management overlays.
- Evaluate model risk associated with machine-learning and artificial-intelligence models where applicable.
- Assess explainability, interpretability, stability, bias, fairness, and governance considerations for advanced analytical models.
- Review model use cases and confirm that models are applied within approved scope, purpose, and limitations.
- Identify unauthorized, inappropriate, or out-of-scope model usage.
- Review model documentation for completeness, accuracy, transparency, and consistency with actual implementation.
- Establish minimum documentation requirements for model development, validation, implementation, monitoring, and governance.
- Review model validation reports and provide clear conclusions, findings, limitations, and recommendations.
- Assign model risk ratings based on materiality, complexity, performance, limitations, and control effectiveness.
- Track model validation findings, remediation actions, risk acceptances, and outstanding issues.
- Challenge remediation plans and assess whether corrective actions adequately address identified model weaknesses.
- Escalate material model risks, unresolved findings, overdue actions, and governance breaches to appropriate committees and senior management.
- Maintain model risk registers, issue logs, validation schedules, approval records, and governance documentation.
- Support Model Risk Management committees and other risk governance forums.
- Prepare management reports covering model inventory, validation status, model risk ratings, performance, findings, and remediation.
- Develop model risk dashboards and key risk indicators for senior management.
- Establish model risk appetite metrics and monitor compliance where applicable.
- Coordinate with Enterprise Risk, Credit Risk, Market Risk, Operational Risk, Finance, Treasury, Compliance, Internal Audit, and Technology teams.
- Work with model developers, data scientists, quantitative analysts, risk owners, business users, and technology teams.
- Provide independent challenge while maintaining constructive relationships with model owners and developers.
- Support regulatory examinations, internal audits, external reviews, and supervisory requests relating to model risk.
- Prepare model documentation, evidence, validation materials, and responses for regulatory or audit reviews.
- Monitor developments in model risk management regulations, supervisory guidance, quantitative techniques, and industry practices.
Key Performance Indicators
- * Model inventory completeness
- Model inventory accuracy
- Model classification completion
- Model risk assessment completion
- Model validation completion rate
- Validation plan adherence
- Model validation timeliness
- High-risk model validation coverage
- Model performance monitoring completion
- Model monitoring threshold compliance
- Model performance deterioration detection
- Model risk rating accuracy
- Model documentation completeness
- Model governance compliance
- Model approval compliance
- Model implementation accuracy
- Model change-management compliance
- Model issue identification rate
- Model finding remediation rate
- Overdue model-risk issue rate
- High-risk finding closure time
- Repeat model findings
- Model risk acceptance compliance
- Model limitation identification
- Model data-quality issue resolution
- Model monitoring exception rate
- Model stability performance
- Model predictive performance
- Model calibration performance
- Back-testing effectiveness
- Challenger-model coverage
- Stress-testing coverage
- Model inventory reconciliation accuracy
- Regulatory model-risk compliance
- Audit finding resolution
- Regulatory issue resolution
- Model governance committee reporting timeliness
- Model risk dashboard accuracy
- Model risk appetite compliance
- Model risk escalation timeliness
- Model review cycle efficiency
- Model-risk process automation
- Stakeholder satisfaction
- Model risk training completion
- Model governance awareness
- Model risk control effectiveness
Ideal CandidateThe successful candidate should have strong experience in model risk management, quantitative risk, model validation, credit risk, market risk, financial risk, statistical modeling, or quantitative analytics, preferably within banking, financial services, insurance, investment management, fintech, or another highly regulated environment.
The candidate should demonstrate:
- Strong understanding of model risk management principles and quantitative risk governance.
- Proven experience performing or overseeing independent model validation.
- Strong understanding of model development, implementation, monitoring, and lifecycle management.
- Experience assessing model conceptual soundness, assumptions, methodologies, and limitations.
- Strong quantitative and statistical analysis capabilities.
- Experience with statistical testing, back-testing, benchmarking, sensitivity analysis, and stress testing.
- Strong understanding of model performance, calibration, stability, discrimination, and predictive accuracy.
- Experience with credit-risk, market-risk, liquidity-risk, capital, pricing, forecasting, fraud, or other financial models.
- Strong understanding of data quality, data lineage, model inputs, and data governance.
- Experience reviewing model documentation and challenging model development approaches.
