Senior Credit Risk Model Developer, Risk Modelling - Digital Bank

About the team

We are looking for a Direct Lending Risk Model Developer to join our team supporting the development and enhancement of risk models for our direct lending business. This role will be responsible for building and refining credit scoring models, loss forecasting models, and early warning systems using both traditional and alternative data sources. The ideal candidate combines technical expertise with business acumen and is motivated by the impact of data-driven lending in the digital banking space.

What you'll do

• Develop, implement, and maintain credit risk models for lending, including application scorecards, behavioral models, and PD/LGD/EAD estimations.
• Leverage structured and unstructured data sources (e.g. financials, transaction data, e-commerce data) for model feature engineering.
• Support model validation, back-testing, performance monitoring, and recalibration activities.
• Work closely with risk policy, data engineering, and credit operations teams to align modelling outputs with business strategies and operational constraints.
• Contribute to model governance documentation, audit preparation, and regulatory compliance under MAS and internal risk frameworks.
• Apply statistical and machine learning techniques (e.g., logistic regression, gradient boosting, random forests) for predictive modelling.
• Translate business objectives into modelling initiatives, communicating findings to both technical and non-technical stakeholders.

Requirements

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Business Analytics, Economics, Finance, or related field. Master's and PhD degree are preferred
• Minimum 3 years hands-on experience in applied machine learning or data science; experience in credit risk or anti-fraud modeling is preferred.
• Hands-on experience with model development lifecycle: data extraction, cleaning, feature engineering, training, validation, and deployment.
• Proficient in SQL and Python (or equivalent). Familiarity with Spark or SAS is a plus.
• Strong understanding of statistical modelling techniques and machine learning algorithms.
• Working experience in Retail/Institutional Banking, Consumer/SME Finance, Supply Chain Finance, e-Commerce, Consulting or Credit Ratings Agency will be an advantage.
• Knowledge of MAS risk model governance or IFRS 9 is a plus.
• Strong analytical, communication, and stakeholder management skills.