About the area:
The CIB Credit COE is responsible for the development, implementation and maintenance of advanced CIB credit risk models, with a particular focus on Low Default Portfolios (LDP). The area combines quantitative modelling, advanced analytics and artificial intelligence capabilities to enhance risk measurement, decision-making and capital optimization.
About the position:
We are seeking a motivated Associate Data Scientist to support the development, maintenance and enhancement of credit risk models for Low Default Portfolios (LDP). The role is suited for professionals with solid quantitative foundations who are looking to grow their expertise in credit risk modelling within an IRB framework, working closely with senior modelers and business stakeholders.
The position focuses on contributing to the development of PD, LGD and CCF models, data analysis, and model implementation for LDP portfolios across different asset classes.
Responsibilities:
- Support the development and maintenance of credit risk models and parameters (PD, LGD, CCF) for Low Default Portfolios.
- Perform data analysis, feature engineering and exploratory studies to support model development and calibration.
- Contribute to model estimation, testing, backtesting and performance monitoring activities.
- Assist in the integration of internal and external data sources into modelling frameworks.
- Support the documentation of models and methodologies in line with internal governance and regulatory requirements.
- Collaborate with senior data scientists, risk managers and technology teams in the implementation of models into production.
- Participate in interactions related to model reviews, validations and regulatory processes.
Qualifications:
- 2–5 years of experience in analytical or quantitative roles, preferably within the financial sector.
- Academic background in Mathematics, Statistics, Engineering, Economics, Physics or related quantitative fields.
- Exposure to credit risk modelling or risk analytics, ideally within Low Default or corporate portfolios.
- Basic knowledge of IRB approaches and regulatory frameworks for credit risk (Basel).
- Proficiency in Python (or similar statistical/programming tools) for data analysis and modelling.
- Strong analytical skills and attention to detail, with the ability to work with complex datasets.
- Willingness to learn and grow in a highly technical and regulated environment.
- Good communication skills and ability to work collaboratively in multidisciplinary teams.
- Fluent in English.
