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 solid 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 solid 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

📌 Assistant Vice President.Retail Risk Analytics-Risk Management (Bengaluru)
🏢 Mashreq
📍 Bengaluru