Analytics Officer

Role : Analytics Officer

Location : Abu Dhabi

This position involves preparing databases and reports for credit risk models and risk segmentation. The Retail Credit Analytics Officer plays a pivotal role in analyzing and monitoring the retail credit portfolio, and supporting the Head of Retail Analytics for all Credit Score development work.

• Continuously monitor the performance of retail credit portfolios, including personal loans, auto loans, credit cards, and mortgages.
• Identify emerging risk trends and provide actionable insights to mitigate potential losses.
• Develop and maintain dashboards and reports highlighting key performance indicators (KPIs) such as delinquency rates, non-performing loans (NPLs), and recovery rates.
• Work closely with ERM team to develop the Portfolio Quality Review (PQR) pack, covering the following topics: Mandate & Scale Tracking MI for tracking both portfolio and new sourcing performance, vintage analysis, test performance tracking, account maintenance tracking, deviation performance tracking, PPR working with Business team.
• Ensuring credit risk is properly quantified and adequate provisions are allocated for expected losses. Work closely with business stakeholders in forecasting/budgeting the provision. Tracking the actual performance with budget to identify high risk segments if needed.
• Prepare comprehensive reports for internal stakeholders and regulatory bodies, ensuring accuracy and timeliness.
• Coordinate with IT and data management teams to ensure the integrity and availability of data sources.
• Engage with product, collections, and underwriting teams to refine credit strategies.
• Review and manage data tables for underwriting and portfolio indicators and develop automated dashboards for reporting.
• Prepare and provide data for development of applications score cards, and support the Head of Retail Analytics in developing analytics-based scorecards.
• Provide data-driven recommendations for the development and revision of credit policies.

• Strong command of data analysis tools such as SQL, SAS, Python, R, and data visualization platforms like Power BI or Tableau.
• Strong analytical and problem-solving abilities.
• Ability to work with large databases and automate report generation process.