Senior Data Scientist, Credit Risk

Job Summary

Our Data and AI team is seeking a SeniorData Scientist (Credit Risk) to develop next-generation credit risk solutions using advanced analytics and machine learning. In this role, you will combine traditional credit risk modelling with modern AI techniques to build robust, explainable, and production-ready models that enhance risk assessment while meeting regulatory requirements.

As a senior executive, you will work across Data, Risk, Finance and Business teams to deliver explainable, production-ready AI solutions that satisfy both commercial objectives and regulatory expectations. The ideal candidate also brings a strategic, collaborative mindset—partnering across teams to explore alternative data ecosystems that unlock superior predictive power and safely promote financial inclusion.

Job Responsibilities

• Design, build, and deploy next-generation credit risk models, blending traditional approaches (PD, LGD, EAD, Scorecards) with advanced Machine Learning algorithms (XGBoost, LightGBM, Random Forests).
• Work closely with our Finance and Credit Risk teams to translate machine learning and statistical outputs into actionable financial strategies and underwriting policies, and prepare clear and thorough documentation for regulatory submission and approval.
• Identify, and feature-engineer non-traditional data sources (e.g., transactional data, open banking, clickstream data, text analytics) to improve model lift, predictive power and financial inclusion.
• Adapt machine learning methodologies to comply with strict regulatory frameworks (e.g. MFRS 9 ECL).
• Establish best practices for the internal validation of advanced AI/ML models. Conduct comprehensive back-testing, sensitivity analyses, and discriminatory power assessments (e.g., Gini, AUC-ROC) to challenge model assumptions and identify limitations prior to production deployment.
• Work with ML Engineers to establish automated pipelines to monitor data drift and model performance, proactively alerting both Finance and Risk teams to shifts in portfolio quality.
• Identify opportunities to pioneer agentic AI workflows for complex financial reasoning, automated credit research, and advanced risk applications.
• Mentor junior data scientists and contribute to the establishment of model development best practices.

Job Requirements

• Bachelor's or Master's in Data Science, Mathematics, Statistics, Computer Science, Actuarial Science, or a highly quantitative field.
• 5+ years driving impact across Data Science and Credit Risk Analytics, with a proven history of launching robust financial risk models within stringent regulatory frameworks.
• Domain Expertise:Strong understanding of credit risk methodologies, model validation strategies and regulatory frameworks.
• Strong proficiency in Python to manipulate complex datasets, conduct deep statistical analyses, and engineer robust, production-ready predictive models.
• Adept with SQL to translate raw data into model-ready features, or conduct critical analytical deep-dives.
• Strong foundation in statistics, predictive modelling, machine learning algorithm and model evaluation techniques.
• Experience applying Explainable AItechniques such as SHAP or LIME to improve model interpretability and regulatory transparency.
• Experience with Cloud platforms (AWS, Google Cloud, Databricks) and MLOps tools (e.g., MLflow, Airflow) is a plus.
• Excellent analytical thinking, communication and stakeholder management skills.