Assistant Manager - Model Monitoring (UAE National)

In line with the UAE Government’s strategy in empowering and developing nationals, Emirates NBD is committed to welcoming the young generation into an innovative, modern and supportive work environment to contribute to the nation's success. We are looking to find the best UAEN talen t to join our ENBD famil y. Organization Unit Purpose The purpose of Group Risk Management unit is to ensure sound risk management practices in the organization and meet regulatory requirements.

The unit has several deliverables that include Basel II compliance, Central bank reporting, IFRS 9 loss provisioning, Internal ratings and embedding them in business processes, AML & compliance, operational loss reporting, risk reviews etc. Job Purpose The prime responsibilities of Assistant Manager –Model Monitoring will be – Assist the Manager Model Monitoring to manage interim tasks related to monitoring of retail and wholesale credit risk scorecards and models that are developed in-house and/or externally on an ongoing basis Tasks related to monitor credit risk models across Emirates NBD Group – comprising Emirates NBD Bank, Emirates Islamic, Emirates Money, Emirates NBD Egypt and Emirates NBD KSA Understand and comply to the established model monitoring best practices and processes within the organization Prepare and collate monitoring results Built reputation and collaborative relationships within the monitoring team Job Content 1.

Comply to model monitoring framework and standards Work on retail and wholesale models to comply with the accepted monitoring methodology, metrics, benchmarks and guidelines customized to specific requirements of various kind of scorecards and models being leveraged currently in the organization Understand and comply to the relevant documentation on model monitoring as a part of internal IP 2.

Monitoring tasks related to – a. Retail application, behaviour, collection and recovery scorecards. b. Wholesale (Large corporate, Sovereign, SME etc.) rating models c. Basel II Models – PD, EAD and LGD. d. IFRS 9 Models – Expected Loss, Long term and PIT PD.+ Follow the annual model monitoring schedule incorporating models to be monitored and adhere to timelines for sharing data, sharing model specifics and model monitoring results Perform model monitoring tasks as per the adopted monitoring methodology and prepare the results as per schedule 3.

Self-assessment and growth in building a result-oriented attitude Keep an active channel of communication within the team to share monitoring results and run self-assessment of shortcomings that suggest observed points of failure Work on independently handling model monitoring tasks and co-ordinate with internal clients on necessary requirements including – SAS codes for creation of data base, scoring, good/bad tagging etc.

; Model development specifics like development period, characteristics, OOT validation period etc.; on regular basis Education Bachelor's Degree in Math, Statistics, Physics, Engineering Experiences 1- 2 years of experience Knowledge & Skills Understanding of statistical techniques in general at least. Python, SAS, VBA, SQL, Maths OR Statistics Excellent verbal and written communication abilities Presentation skills

1. Reliability of risk model outputs through timely execution of scheduled model monitoring activities Conduct periodic monitoring of IFRS 9 and other risk models using established statistical and business performance metrics. Compile and validate model performance data to identify deviations from expected outcomes, ensuring early detection of potential issues. Document findings and observations in accordance with internal audit and regulatory requirements. Escalate significant anomalies or emerging risks to the manager for further investigation and action. Performance Measures: Consistency and completeness of model monitoring documentation Accuracy of anomaly detection and escalation Stakeholder confidence in reliability of model performance reporting
2. Regulatory compliance in model monitoring processes aligned with internal and external standards Apply current regulatory guidelines (e.g., MAS, PRA, RBI) to daily model monitoring tasks, ensuring adherence to all relevant standards. Maintain up-to-date records of model monitoring activities for audit and regulatory review. Support the preparation of regulatory submissions by providing validated model performance data as required. Identify and report process gaps or compliance risks to the manager for remediation. Performance Measures: Absence of regulatory findings in model monitoring processes Quality of records available for audit and regulatory inspection Timeliness and accuracy of data provided for regulatory submissions
3. Data integrity and traceability in risk model monitoring datasets Extract, cleanse, and organise model input and output data from risk systems to support monitoring activities. Verify data lineage and traceability to ensure all datasets used in monitoring are accurate and complete. Maintain clear audit trails for all data transformations and calculations performed during monitoring. Collaborate with IT and data management teams to resolve data quality issues impacting model monitoring. Performance Measures: Completeness and accuracy of data used in model monitoring Clarity and auditability of data transformation documentation Resolution rate of data quality issues impacting monitoring
4. Business insight for risk management decisions enabled by clear and actionable model monitoring reports Prepare concise monitoring reports summarising model performance, key findings, and recommended actions for direct stakeholders. Translate technical results into business-relevant insights for risk and finance teams. Respond to queries from business users regarding model monitoring outputs and methodologies. Support the continuous improvement of reporting templates and communication channels for model monitoring results. Performance Measures: Clarity and relevance of monitoring reports for business stakeholders Responsiveness to stakeholder queries and feedback Demonstrated impact of monitoring insights on risk management actions Bachelor's degree in Statistics, Mathematics, Finance, Economics, or related field Relevant certifications in risk management or data analytics (e.g., FRM, CFA, SAS Certification) preferred Ongoing professional development in regulatory and risk management practices