Quantitative Analyst – Credit Modelling

SUMMARY:
Seeking a Quantitative Analyst in Credit Modelling.

POSITION INFO:
About the Role:
A fast-growing fintech company is looking for a Quantitative Analyst with a strong background in credit risk modelling to join its Data & Analytics team. The business provides innovative financial services including mobile money, remittances, and micro-lending, targeting under-served and emerging markets.
This is a high-impact, mid–senior level role for someone who thrives in a fast-paced, data-driven environment and is passionate about building robust models that support inclusive lending.

Key Responsibilities:

• Design, build, and validate credit scoring models to support decisions for acquisition, behavioural, and collections strategies.
• Leverage customer data and alternative data sources to improve credit risk assessments and portfolio quality.
• Work closely with Risk, Product, and Finance teams to support strategy around lending, pricing, and risk appetite.
• Monitor model performance and recalibrate as needed to ensure stability and regulatory alignment.
• Develop portfolio segmentation strategies and contribute to policy design for credit and collections.
• Build dashboards and automated tools to track credit KPIs and risk indicators.
• Present insights and model outcomes clearly to business and technical stakeholders.

• Bachelor’s or Honours Degree in a quantitative discipline (e.g., Statistics, Mathematics, Actuarial Science, Data Science, Engineering, or Computer Science).
• 3–6 years of experience in a quantitative, risk, or analytics role, with a strong focus on credit modelling.
• Hands-on experience developing, validating, and deploying credit risk models (application and behavioural).
• Knowledge of credit risk metrics such as PD, LGD, and EAD.
• Strong skills in Python or R, and SQL for data analysis and modelling.
• Solid grasp of statistical and machine learning techniques such as logistic regression, decision trees, and segmentation.
• Strong communication skills with the ability to explain analytical results to non-technical teams.

• Experience using alternative data in risk modelling, particularly in emerging markets.
• Exposure to digital lending, mobile money, or informal credit markets.
• Familiarity with Power BI, Tableau, or other data visualization tools.
• Experience working in a cloud environment (AWS, GCP, Azure).