As a Credit & Portfolio Management Analyst, you will help drive data-driven lending decisions through portfolio analytics, credit risk monitoring, and performance insights. You will work closely with Risk, Business, and Data teams to analyze portfolio trends, assess credit performance, and support strategies that balance portfolio growth and risk management.
What you will do:
- Develop and maintain portfolio reports, dashboards, and analytics tools.
- Perform data analysis to identify risks, opportunities, and portfolio performance drivers.
- Support scorecard monitoring, portfolio segmentation, forecasting, and risk analytics initiatives.
- Create decision trees, analytical frameworks, and portfolio monitoring processes to support risk and business decisions.
- Provide actionable insights and recommendations based on portfolio performance and customer behavior.
- Assist in enhancing portfolio monitoring frameworks, credit policies, and risk management processes.
- Work closely with cross-functional teams to support strategic portfolio initiatives and business growth.
What we are looking for:
- Bachelor's degree in Statistics, Mathematics, Actuarial Science, Data Science, Computer Science, Economics, Engineering, or a related quantitative field.
- Experience in Credit Risk Analytics, Portfolio Analytics, Risk Modeling, Consumer Lending Analytics, ECL, Scorecards, or related banking/fintech functions.
- Experience in portfolio monitoring, asset quality reporting, scorecard monitoring, risk analytics, or portfolio performance analysis.
- Strong analytical and problem-solving skills with the ability to turn data into actionable business insights.
- Proficiency in SQL and at least one programming language such as Python, R, or SAS.
- Experience with reporting and visualization tools such as Power BI, Tableau, or similar platforms.
- Knowledge of consumer lending, credit risk, portfolio management, predictive modeling, or ECL frameworks is an advantage.
- Comfortable working with large datasets and partnering with business, risk, and data stakeholders.