Assistant Manager - Retail Analytics
Employer not yet confirmed · Dubai, UAE
The Emirates Islamic Retail Risk Analytics Unit supports Consumer Risk Management through portfolio analytics, risk modelling, management information and regulatory reporting. The unit delivers actionable insights to monitor portfolio performance, identify emerging risks and support credit policy, collections and business decisions. It also improves analytical capabilities through automation, modern data tools and collaboration with Business, Collections, Finance and Technology.
- Support the migration of existing analytics and reporting processes to Python, developing reusable scripts and automated workflows.
- Analyze retail portfolio performance, including delinquency, vintage, roll rates, segmentation and collections trends, identifying emerging risks and recommending actions.
- Monitor application and behavioral scorecards and support the development of statistical and predictive models for retail credit risk and collections.
- Maintain code quality through Git and GitHub, including version control, testing, documentation and peer reviews.
- Support colleagues in adopting Python-based processes through knowledge sharing, troubleshooting and technical guidance.
- Prepare management and regulatory reports, ensuring data accuracy, reconciliations and timely delivery.
- Ensure analytical solutions and models comply with the Bank's model governance requirements.
- Bachelor’s degree in computer science, Engineering, Mathematics, Statistics, Data Science, Finance or a related quantitative discipline.
- Minimum 3 years of relevant experience in data analytics, risk analytics or analytical automation, including at least 2 years of hands-on Python development.
- Strong Python and SQL skills, including experience with Pandas and NumPy.
- Practical experience developing automated analytical workflows and migrating reporting processes to Python.
- Proficiency in Git and GitHub, including version control, collaborative development and code reviews.
- Ability to develop reusable, maintainable code and perform data reconciliation, testing and quality checks.
- Working knowledge of statistics and predictive modelling.
- Strong analytical, problem-solving and communication skills, with the ability to collaborate and share technical knowledge.