Credit Risk Analytics

We have an immediate job opportunity in Dubai, United Arab Emirates, for a position focused on Credit Risk Analytics. This full-time, onsite role requires 6 to 14 years of experience and allows for a notice period ranging from immediate to 30 days.

The position involves developing and maintaining scorecards (application and behavior) to support risk-based strategies across the customer lifecycle. Key responsibilities include:

• Leveraging advanced analytics and machine learning to improve predictiveness and business outcomes from risk models.
• Designing and implementing data-driven credit strategies that balance risk, profitability, and customer experience.
• Performing data extraction, preparation, and feature engineering using large structured datasets from internal and external sources.
• Applying statistical techniques such as logistic regression, decision trees, and machine learning algorithms to build robust, interpretable models.
• Conducting model performance evaluation, including KS, Gini, PSI, stability, lift analysis, and backtesting to ensure model accuracy and consistency.
• Documenting the entire model development lifecycle (MDLC) in compliance with internal model governance and regulatory requirements (e.g., IFRS9, Basel).
• Collaborating with risk policy, credit underwriting, and collections teams to ensure models are effectively translated into business strategies.
• Providing analytical support in model calibration, cut-off analysis, stress testing, and scenario analysis.
• Participating in internal and external model validation exercises, audits, and regulatory reviews by preparing detailed documentation and responses.
• Continuously enhancing models by incorporating alternative data, new modeling techniques, and automation tools.
• Performing ad hoc analyses as requested by senior management.

Required Skills:

• 5-10 years of hands-on experience in credit risk modeling, with a strong focus on scorecard development in banking or consumer lending.
• Strong programming skills in SAS (preferred), Python, R, and SQL for model building, data preparation, and validation.
• Deep understanding of statistical modeling techniques such as logistic regression, WOE/IV transformation, scorecard binning, and variable selection.
• Experience in developing models for retail lending products such as personal loans, credit cards, auto loans, or mortgages.
• Familiarity with model governance processes, documentation standards, and regulatory requirements (e.g., Basel, IFRS9, CB guidelines).
• Ability to work independently on data sourcing, feature creation, model training, and performance evaluation.
• Experience with data visualization and reporting tools (e.g., Power BI, Tableau) is a plus.
• Strong communication and documentation skills to explain model logic to stakeholders, reviewers, and non-technical teams.

Qualifications:

• Bachelor’s or Master’s degree in computer science, Information Systems, or a related field.

Soft Skills:

• Excellent communication and leadership skills.
• Strong interpersonal and collaboration skills.
• Ability to work under pressure and meet tight deadlines.
• Positive attitude and strong work ethic.