Assistant Vice President.Retail Risk Analytics-Risk Management

Key Result Areas

Responsible for the development, implementation, and maintenance of credit risk models and scorecards, including PD, LGD, and EAD across the retail portfolio lifecycle (acquisition, behavioral, collections).

• Lead the design and enhancement of credit risk modelling frameworks, incorporating scorecards and appropriate statistical/analytical techniques to support underwriting and portfolio management decisions.
• Monitor, document, and communicate the performance, assumptions, and limitations of credit risk models to stakeholders, ensuring transparency and model interpretability.
• Perform model monitoring, backtesting, and periodic recalibration, ensuring models remain accurate, stable, and compliant over time.
• Provide recommendations for model redevelopment or enhancement based on portfolio trends, data drift, and emerging risk patterns.
• Prepare and support Basel regulatory reporting, including RWA estimation and model-related submissions aligned with internal and regulatory requirements.
• Lead/support IFRS 9 ECL modelling, including staging, macroeconomic overlays, scenario-based expected credit loss estimation and stress testing including climate risk.
• Deploy credit risk models into production systems / rating platforms, working closely with IT and data teams to ensure data integrity and system robustness.
• Support design and implementation of credit risk strategies and decision rules (e.g., cut-offs, risk segmentation, line management) aligned with model outputs.
• Perform and oversee model validation and testing activities (functional, statistical, and regulatory) prior to deployment.
• Establish robust model governance practices, including documentation, audit trails, and compliance with regulatory standards.
• Identify opportunities to enhance credit risk models using advanced analytics or machine learning techniques, where appropriate and justifiable.
• Establish MLOps standards for model deployment, monitoring, versioning, and performance tracking in production environments.
• Develop data-driven insights to monitor portfolio quality, risk trends, and early warning indicators.
• Collaborate with policy, finance, and business teams to support portfolio optimization, provisioning, and capital management decisions.
• Ensure timely communication of model performance, validation findings, and risk insights to senior management and committees.
• Mentor junior analysts and contribute to building a strong, technically sound credit risk modelling team.

• Perform other duties as assigned.

Operating Environment, Framework and Boundaries, Working Relationships

Regular interaction and working relationship with

• Retail Credit Policy

Segment Heads – Business & Marketing

• Group Finance and CAD
• Credit Systems / IT / Data Teams
• Model Validation, Internal Audit, and Compliance
• Regulatory stakeholders (where required)
• Executive Management / Risk Committees

Problem Solving

Candidate must

Demonstrate strong analytical and structured problem-solving skills in credit risk modelling and portfolio analytics

• Possess deep understanding of credit scorecard development, validation techniques, and model risk management practices
• Have strong end-to-end experience in model development, validation, implementation, and performance monitoring
• Be able to diagnose model performance issues (e.g., drift, instability, segmentation breakdown) and recommend corrective actions
• Demonstrate technical proficiency in SAS, SQL, and Python/R, particularly in handling large datasets
• Exhibit strong stakeholder management skills and ability to communicate complex modelling concepts clearly
• Translate quantitative outputs into practical business and risk decisions

Decision Making Authority & Responsibility

• Responsible for ownership of credit risk models and scorecards across the retail portfolio
• Ensure models remain compliant with CBUAE Model Management Standards (MMS/MMG), IFRS 9, and Basel requirements
• Approve model changes, recalibrations, and redevelopment decisions in line with governance frameworks
• Ensure robust model monitoring, documentation, and audit readiness
• Maintain integrity, confidentiality and controlled usage of models (“black box” governance)
• Ensure all model outputs used in decisioning are accurate, consistent, and justified
• Contribute to governance frameworks managing model risk, data risk, and implementation risk

Knowledge, Skills and Experience

10–12+ years of experience in credit risk modelling within retail banking / financial services

• Deep expertise in statistical modeling, machine learning techniques, and large-scale data analysis.
• Strong expertise in credit risk modelling techniques, including PD, LGD, EAD, scorecards, and segmentation approaches
• Proven experience in IFRS 9 ECL modelling and Basel frameworks
• Strong knowledge of model lifecycle management (development, validation, deployment, monitoring)
• Advanced technical skills in SAS, SQL, and Python/R
• Experience in working with large datasets and data platforms (e.g., Hadoop or equivalent)
• Strong statistical and analytical skills with ability to translate data into insights
• Proven track record of building, deploying, and maintaining production ML models with real-time or near-real-time decisioning systems.
• Experience with credit risk strategy development and portfolio analytics
• Familiarity with decision systems / rule engines is an advantage
• Professional certifications such as FRM (Financial Risk Manager) or CFA (Chartered Financial Analyst) are a strong plus
• Experience with MLOps tooling (e.g., MLflow or similar platforms) is highly desirable
• Degree in Quantitative disciplines (Statistics / Mathematics / Actuarial Science / Economics)
• Strong communication skills with ability to present technical conce