Credit Portfolio Analyst

Job Summary:
We are seeking a detail-oriented and analytical Credit Portfolio Analyst to support the monitoring, analysis, and optimization of our credit portfolio. The role involves working with large datasets, assessing credit risk trends, and delivering actionable insights to improve portfolio performance and minimize risk exposure.
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Key Responsibilities:

• Analyze credit portfolio performance, including delinquency trends, default rates, and risk segmentation
• Develop and maintain dashboards and reports to track key credit metrics
• Perform data extraction, transformation, and analysis from multiple data sources
• Identify early warning signals and emerging risks within the portfolio
• Support credit strategy development through data-driven insights
• Collaborate with risk, finance, and underwriting teams to optimize credit policies
• Conduct stress testing and scenario analysis on portfolio performance
• Ensure data accuracy, integrity, and consistency across systems
  
  

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Required Hard Skills:

• Advanced Excel skills (pivot tables, VLOOKUP/XLOOKUP, INDEX-MATCH, macros, data modeling)
• Strong proficiency in SQL for querying and managing relational databases
• Experience with data visualization tools (e.g., Tableau, Power BI)
• Working knowledge of statistical analysis and modeling techniques
• Familiarity with credit risk metrics (PD, LGD, EAD, vintage analysis, roll rates)
• Experience handling large datasets and performing data cleaning/transformation
• Understanding of database structures and data warehousing concepts
• Basic programming skills in Python or R (preferred for automation and analysis)
  
  

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Qualifications:

• Bachelor’s degree in Finance, Economics, Statistics, Mathematics, or a related field
• 2–5 years of experience in credit risk, portfolio analysis, or financial analytics
• Strong analytical thinking and problem-solving abilities
• Attention to detail and ability to work with complex datasets
  
  

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Nice to Have:

• Experience in banking, fintech, or lending institutions
• Knowledge of regulatory frameworks (e.g., Basel II/III)
• Familiarity with machine learning techniques for credit risk modeling
  
  

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Key Competencies:

• Data-driven decision making
• Critical thinking and risk assessment
• Effective communication of complex insights
• Time management and ability to handle multiple priorities