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