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 concepts to business stakeholders
•    Self-driven, detail-oriented, and highly motivated team player
 

• Lead the development, validation, and governance of credit risk models, scorecards, and portfolio analytics for retail portfolios.
• Design and enhance credit risk modelling frameworks, incorporating statistical techniques and scorecards.
• Monitor and communicate model performance, assumptions, and limitations to stakeholders.
• Perform model monitoring, backtesting, and recalibration to ensure accuracy and stability.
• Provide recommendations for model redevelopment based on portfolio trends and risk patterns.
• Support Basel regulatory reporting and IFRS 9 ECL modelling, including stress testing.
• Deploy credit risk models into production systems, ensuring data integrity and system robustness.
• Support the design and implementation of credit risk strategies and decision rules.
• Establish robust model governance practices, including documentation and audit trails.
• Mentor junior analysts and contribute to building a technically proficient credit risk modelling team.

• 10-12+ years of experience in credit risk modelling within retail banking or financial services.
• Deep expertise in statistical modelling, machine learning, and large-scale data analysis.
• Strong knowledge of credit risk modelling techniques (PD, LGD, EAD, scorecards) and IFRS 9 ECL modelling.
• Experience with model lifecycle management, including development, validation, and deployment.
• Advanced technical skills in SAS, SQL, and Python/R, with proficiency in handling large datasets.
• Strong stakeholder management and communication skills, able to convey complex modelling concepts.
• Proven track record in building and maintaining production ML models for real-time decisioning systems.
• Familiarity with decision systems and rule engines is advantageous.
• Professional certifications such as FRM or CFA are highly desirable.
• Degree in Quantitative disciplines (Statistics, Mathematics, Actuarial Science, or Economics).