Requirements:

• A degree in Data Science, Statistics, Mathematics, or a related quantitative discipline
• A minimum of 3 years of experience in Data Science, Risk Analytics, or Credit Risk Management.
• Experience building and validating credit risk models, including scorecards and provisioning.
• Proficiency in Databricks, SQL, R, or Python.
• Familiarity with IFRS 9, Basel, or equivalent credit risk regulatory frameworks.
• Exposure to cloud-based data platforms (AWS, GCP, Azure).
• Strong background in statistical modelling, machine learning, and predictive analytics.
• Knowledge of advanced machine learning techniques and model interpretability methods.

Responsibilities:

• Develop, implement, and maintain acquisition scorecards to evaluate MCA applicants.
• Monitor provision models aligned with regulatory and accounting standards.
• Enhance portfolio monitoring tools to track credit performance and early warning signals.
• Analyse transaction data, repayment patterns, and business health metrics to inform risk strategies.
• Identify trends, correlations, and anomalies that impact credit performance and portfolio stability.
• Collaborate with product, finance, and operations teams to embed data-driven decision-making.