Data Scientist

We are seeking an experienced Data Analyst/Scientist to lead the development of ML-driven applications and behavioural scorecards for retail lending portfolios. The role requires strong statistical rigour, end-to-end model ownership, and the ability to translate credit risk appetite into scalable data-driven strategies.

Responsibilities:

• Design and deploy statistical models, including but not limited to application and behavioural scorecards, using advanced ML techniques.
• Lead end-to-end lifecycle: problem framing, data extraction, feature engineering, model development, validation, deployment, and monitoring.
• Develop statistically robust models using techniques such as (but not limited to): Gradient Boosting (XGBoost, LightGBM, CatBoost), Random Forest, ensemble methods, and neural networks (MLP).
• Apply rigorous evaluation frameworks (AUC, KS, Gini, Lift, PSI, stability analysis).
• Address imbalanced datasets, reject inference, segmentation, and portfolio drift.
• Build challenger models and drive continuous performance optimisation.
• Develop and refine credit underwriting strategies aligned to risk appetite, approval rate targets, and portfolio profitability.
• Conduct portfolio analytics and recommend cut-off, limit, and pricing strategies.
• Collaborate with Risk, Policy, Product, and Tech teams to translate business requirements into deployable decisioning frameworks.
• Ensure robust monitoring, back-testing, and periodic recalibration.

Requirements:

• Bachelor's in engineering, business, analytics, data science, or a related field.

Technical Requirements:

• Strong foundation in statistics, hypothesis testing, sampling theory, and classification methodologies.
• Proven experience in ML-based credit modeling, model governance, and monitoring.
• Expertise in handling large structured financial datasets.
• Proficiency in Python, PySpark, Scikit-learn, and boosting frameworks.
• Experience building scalable training pipelines and supporting production deployment.