Lead the development and maintenance of credit risk models and parameters (PD, LGD, CCF) for Low Default Portfolios across different asset classes.
Design and implement methodologies for model estimation, calibration and monitoring, ensuring robustness and regulatory compliance.
Perform advanced data analysis, feature engineering and segmentation studies to support model performance and interpretability.
Coordinate the integration of new internal and external data sources to enhance existing modelling frameworks.
Act as a technical reference for model governance, including documentation, model reviews, validation processes and remediation actions.
Support interactions with internal validation, audit and supervisory authorities (e.g. IMIs), providing quantitative analysis and methodological explanations.
Collaborate with technology teams to ensure efficient and robust model implementation in production environments.
Provide technical guidance and mentoring to junior team members, contributing to their professional development.

5 years of experience in quantitative or analytical roles, preferably within credit risk modelling or risk management in financial institutions.
Strong experience in Low Default Portfolio modelling.
Solid knowledge of IRB regulatory frameworks and credit risk modelling requirements.
Proven experience delivering end-to-end modelling projects, from data analysis and methodology design to implementation and monitoring.
Advanced proficiency in Python (or similar tools) for statistical modelling and data processing.
Ability to translate complex quantitative results into clear insights for business and senior stakeholders.
Experience working in multidisciplinary environments, coordinating with business, IT and risk teams.
Experience in database data quality (DQ), and expertise in the new default definition is a plus.
Fluent in English.

Manager, Data Scientist – Low Default Portfolios Models

Key skills