• Bachelor’s Degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Engineering, or similar.
• 1 - 3 years of experience in credit analytics, data analysis, quantitative modelling, or related fields.
• Knowledge of Python, R, or SAS (basic to intermediate).
• Ability to write SQL queries and work with structured datasets.
• Understanding of credit scoring, logistic regression, and model validation concepts.
• Experience in banking, microfinance, or digital lending is an advantage.

Data Preparation & Management:

• Extract, clean, and organize datasets for credit modelling and analysis.
• Work with data engineering and MIS teams to ensure data integrity and completeness.
• Conduct data quality checks and produce summary statistics.

Scorecard & Model Development Support:

• Assist seniors in building credit scoring models (application, behavior, collection).
• Perform exploratory data analysis (EDA) and variable selection.
• Support model development using logistic regression, decision trees, and basic ML techniques.
• Prepare model datasets, feature engineering, and transformations.

Model Monitoring & Performance Tracking:

• Monitor model KPIs such as Gini, KS, AUC, PSI, and score distribution.
• Track override patterns, approval rates, and model drift.
• Prepare monthly and quarterly model performance reports.

Portfolio Analytics:

• Assist with analysis of delinquency trends, roll rates, NPL ratios, and credit losses.
• Support cohort/vintage analysis for MFB and money lending products.
• Provide insights to help refine underwriting policies and risk strategies.

IFRS 9 & Regulatory Support (Basic):

• Support model documentation and basic calculations for expected credit loss (ECL).
• Assist with regulatory submissions related to credit scoring or risk models.
• Maintain compliance with data and modelling standards.

Documentation & Reporting:

• Prepare model documentation (methodology, testing, assumptions).
• Draft monitoring reports and dashboards for internal users.
• Ensure all development steps follow model governance rules.

Collaboration:

• Work with underwriters, credit analysts, and product teams to understand business requirements.
• Provide quantitative insights for new product development and risk appetite changes.
• Support Senior Modellers and Analysts in project execution.