Junior Quant / Scorecard Analyst Yaba

The Concept Group is a holding company for companies established in 1992. Subsidiaries under the Group include: Rosabon Financial Services - Nigeria’s Leading Financial Intermediary and Equipment Leasing Company, Concept Nova - Bespoke Enterprise IT Solutions Company, Percy Aitkins - Bureau De Change. We are recruiting to fill the position below:

Job Title: Junior Quant / Scorecard Analyst

Location: Yaba, Lagos

Employment Type: Full-time 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.

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.

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.

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.

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.

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

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)

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

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

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

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

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

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.

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.

Not Specified. How to Apply

Interested and qualified candidates should send their updated CV to:[Email hidden - Login to reveal] using the Job Title as the subject of the email.