Junior Quant / Scorecard Analyst at The Concept Group

Job Purpose

• The Junior Quant / Scorecard Analyst supports the development, monitoring, and maintenance of credit scoring models and quantitative risk tools.
• The role focuses on data preparation, modelling assistance, portfolio analytics, and documentation to enhance credit decision-making and improve portfolio performance across retail, SME, MFB lending, and fintech lending products.

Key Responsibilities
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.

Key Performance Indicators (KPIs)
Model Development & Support:

• Accuracy and timeliness of data preparation for modelling.
• Contribution to scorecard development (quality of EDA, feature selection, model testing).
• Reduction in model development timelines.

Model Performance Monitoring:

• Timely submission of monthly/quarterly monitoring reports.
• Accuracy of model KPIs (AUC/Gini/KS/PSI) tracking.
• Early identification of model drift or performance issues.

Data Quality & Governance:

• Reduction in errors in modelling datasets.
• Compliance with data governance and documentation standards.
• Number of data quality issues detected and resolved.

Portfolio Impact:

• Improvements in approval rate or bad rate linked to scorecard refinements.
• Accuracy of risk segmentation insights.
• Effectiveness of analytical support for credit policy changes.

Efficiency & Automation:

• Number of automated scripts/reports developed (e.g., Python, SQL).
• Reduction in manual reporting effort.
• Improvement in turnaround time for analytical tasks.

Collaboration & Stakeholder Support:

• Feedback from senior modellers, underwriters, and product teams.
• Timeliness and quality of ad-hoc analysis delivered.
• Contribution to cross-functional risk initiatives.

Qualifications & Experience

• 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.

Skills & Competencies:

• Strong quantitative and analytical skills.
• Basic knowledge of credit risk metrics and scorecard frameworks.
• Proficiency in Python/R and SQL for modelling and analysis.
• Ability to work with large datasets and identify patterns.
• Good communication and documentation skills.
• Willingness to learn advanced modelling and machine learning techniques.
• Detail-oriented and committed to data accuracy.