Center Head of Modelling & Model Validation

• Define and execute the modeling, model validation, and risk analytics strategy in alignment with the Bank's strategic priorities, AI-enabled risk transformation agenda, and Basel roadmap.
  
• Establish and maintain the Group Model Risk Management Framework and enterprise-wide model lifecycle governance, including model inventory, model risk classification, model risk assessment, tiering, approval, implementation, monitoring, change control, retirement, and consolidated model risk reporting.
  
• Represent RMD in model governance committees and engagements with SBV, auditors, and other relevant stakeholders.
  
• Ensure alignment of modeling, validation, and risk analytics capabilities across the Bank and its subsidiaries, with clear ownership and accountability.
  

• Provide strategic direction for the development, enhancement, implementation, and monitoring of risk, regulatory, and business models across the Group.
  
• Oversee credit risk models, including application, behavioral, collection, attrition, cross-sell, propensity, and early-warning models; Basel II/III/IV, IFRS 9, stress testing, capital and portfolio analytics models; and advanced AI/ML, fraud analytics, and alternative-data models.
  
• Establish modern development methodologies, coding and documentation standards, reusable assets, and common feature libraries.
  
• Drive industrialized model development and deployment through MLOps, automated pipelines, CI/CD, controlled retraining, and performance monitoring, in coordination with Business, IT, and EDA.
  

• Ensure independent, risk-based, and timely validation of all material models across the Group in accordance with the Model Validation Framework.
  
• Approve the annual validation plan and oversee validation of credit, market, liquidity, operational, regulatory, and business models.
  
• Establish validation standards for AI/ML models, including explainability, stability, data integrity, bias and fairness where applicable, implementation accuracy, and ongoing monitoring.
  
• Ensure effective challenge of model methodology, assumptions, data, implementation, and performance; track findings and remediation to closure; and provide enterprise-wide reporting on model inventory, model risk classification, risk assessments, lifecycle status, and material model-risk exposures.
  
• Embed model-risk controls into MLOps, including end-to-end lineage, versioning, dev-to-production consistency, tiered change control, retraining guardrails, and continuous evidence for audit and review.
  

• Lead research and controlled adoption of emerging AI, machine learning, GenAI, and Agentic AI applications in risk management.
  
• Sponsor innovation pilots in underwriting, fraud detection, collections, early warning, and portfolio monitoring, with appropriate human oversight and governance.
  
• Promote automation across model development, validation, deployment, monitoring, documentation, and reporting.
  
• Benchmark the Bank's risk analytics and model risk management capabilities against international standards and leading financial institutions.
  

• Define workforce and succession plans, assign KPIs, evaluate performance, and coach technical and leadership talent.
  
• Foster a culture of analytical rigor, independence, innovation, collaboration, continuous learning, and accountability.
  

Requirements

• Master's degree or higher in Statistics, Mathematics, Quantitative Finance, Economics, Data Science, Computer Science, or a related discipline.
  
• Professional certification such as FRM, CFA, PRM, PMP, or an equivalent qualification is preferred.
  

• Deep expertise in credit risk modeling, independent validation, and model risk management.
  
• Strong knowledge of Basel II/III, IRB, IFRS 9, stress testing, capital adequacy, and portfolio analytics.
  
• Strong understanding of AI/ML governance, explainability, model monitoring, risk data architecture, and MLOps.
  
• Knowledge of advanced analytics applications in early warning, fraud, collections, and credit decisioning; familiarity with modern analytics platforms and alternative data.
  

• Strong strategic leadership, stakeholder management, and influencing skills.
  
• Advanced quantitative, analytical, problem-solving, and executive communication skills.
  
• Working knowledge of SAS, SQL, Python or R, modern data platforms, model deployment, and system integration.
  

• Strategic thinking and vision alignment.
  
• Leadership, talent development, and succession building.
  
• Analytical rigor, sound judgment, and decision-making.
  
• Independence, integrity, and professional ethics.
  
• Innovation mindset, collaboration, and continuous improvement orientation.