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

Originally posted on Himalayas