Portfolio Monitoring: Analyze credit portfolio performance (NPLs, delinquency rates, vintage analysis, roll rates) and prepare periodic risk trend reports.
Data Exploration & Insight Generation: Conduct deep-dive analysis into customer behavior and credit data to identify risk patterns, anomalies, and opportunities.
Policy & Strategy Recommendations: Provide data-driven recommendations for adjustments to credit policies (e.g., cut-off scores, credit limits, pricing strategies).
Reporting & Dashboarding: Create, maintain, and automate dashboards using BI tools to monitor risk KPIs for stakeholders (Risk, Product, Business).
Cross-functional Collaboration: Support Risk Modelers/Data Scientists in the implementation and monitoring of credit scoring models.
Requirements
Minimum bachelor's degree in Statistics, Mathematics, Actuarial Science, Economics, Industrial Engineering, Computer Science, or another quantitative field.
Minimum 2 years of experience in Data Analytics, Risk Management, or Credit Scoring in the Banking, Fintech Lending, or Multifinance industries.
Highly proficient in SQL (mandatory) for complex data manipulation, querying, and extraction.
Familiarity with Python or R (especially libraries such as Pandas, Scikit-learn, and XGBoost).
Proficient in creating dashboards using Superset, Tableau, PowerBI, or Metabase.Advanced level (VBA, Power Query, advanced Pivot Tables).
Ability to translate complex data and numbers into actionable business strategies and risk policies.
Strong problem-solving skills with a keen eye for detail and data anomalies.
Ability to clearly explain data insights and risk trends to non-technical stakeholders.
