Credit Risk Analytics Specialist

Responsibilities:

• Manage and prepare data for model development in Credit Risk Scoring Models (Application, Behavioral and Collection Models).
• Develop data quality report with basic analyses of user requirements.
• Work closely with vendor to understand wing-to-wing process on how to validate and monitor credit scoring model.
• Organize periodical meetings with related parties, e.g. business users, credit approval officers, and risk managers, to obtain feedback of all models in use and to collect recommendations for improving existing models.
• Create PD, EAD, and LGD Segmentation models for Retail/Non-Retail lending portfolios to calculate capital/provision requirement regulated by Basel II AIRB/IFRS9 approaches, including data preparation prior to develop and monitor models.
• Generate Model for Stress Test report in the area of Pillar 2 Analysis - Stress Test, Economic Capital.
• Conduct annual & ad hoc stress-test using credit score models.
• Estimate impact of Macro Economic to bank Economic Capital.
• Perform back testing model to ensure that model segmentation predictive power
• Propose the recommendation to enhance/develop model if model deterioration occurs.
• Produce credit scoring model monitoring report
• Enhance database system to be able to improve data management using in model analysis.

Qualifications:

• Bachelor’s degree or higher in Finance, Statistics, Mathematics, Economics, MIS or any related fields.
• At least 2 years of experience in credit risk analytics (either on Retail or Wholesale portfolios) including credit risk models and risk-rating system development, validation, implementation, and maintenance preferred.
• Equipped with analytical mind and being able to raise relevant questions
• Good English Skill and project management skills.
• Extensive and hands-on analytical programming skills in Python, PySpark, SAS, R, SPSS, Matlab or similar required. Big Data & Data Mining experience is a plus
• Experience in AIRB Basel/IFRS9 Compliance, Stress Testing, RAROC analysis is a plus.