Data Scientist - Credit Risk Model and PPU/SA Monitoring

EUR 55,600-79,400 per year
MIDDLE
✅ Hybrid

🕙 36-40 hours per week

Tech Stack

Azure BI @ 3 Communication @ 6 Data Science Databricks @ 3 Git @ 3 IFRS @ 3 Power BI @ 3 Python @ 6 Reporting @ 6 SQL @ 6 Stress Testing

Details

Join the newly established Monitoring, Reporting and Modelling Framework Team and apply analytical and data science skills to monitor credit risk models across the bank. The work covers IRB, IFRS 9, Stress Testing, Permanent Partial Use of the Standardised Approach (PPU/SA), and Economic Capital, enabling data-driven decision-making.

Responsibilities

  • Monitor and report the use of own funds requirements, including regulatory topics such as the IRB roll-out plan, types of exposure segmentation, and the controlled use of IRB and Standardised Approaches in line with internal standards and regulatory requirements.
  • Monitor the performance of credit risk models, including IRB, non-IRB, IFRS 9, Economic Capital, Stress Testing, and scorecards, across retail and non-retail portfolios.
  • Analyse the root causes of performance deterioration.
  • Further develop tooling for automated model and credit risk landscape monitoring.
  • Oversee the review and development of modelling frameworks, including standards, guidelines, and procedures supporting the development and monitoring of credit risk models.
  • Work with SQL, Power BI, Python/PySpark on Azure/Databricks, and/or SAS.
  • Clearly communicate and present analytical results to a broad range of stakeholders.
  • Develop towards Expert, Project Management, or Coaching career paths.

Working Environment

The Monitoring, Reporting and Modelling Framework team is part of the Credit Risk Control Unit and Model Strategy department, which oversees the life cycle of credit risk models. The team works closely with risk modelling teams and consists of approximately 16 international colleagues. The office is located in the Atlas Arena-Asia Building, with a planned relocation to the new head office at Foppingadreef during 2026/2027. The team operates in a hybrid working model and meets in the office at least once per week.

Requirements

  • MSc or PhD in a quantitative field.
  • Strong programming skills in SQL and/or Python, with experience in data-driven research and working with large datasets.
  • Strong analytical skills, including the ability to identify trends, assess data quality, and translate complex data into actionable insights and reporting.
  • Experience with, or willingness to learn, modern data and analytics tools such as Databricks, Power BI, Git, and SAS.
  • Knowledge of, or willingness to develop expertise in, credit risk management, banking credit processes, and relevant regulatory frameworks such as CRR3, the ECB Internal Models Guide, EBA Guidelines, and IFRS 9.
  • Knowledge of, or willingness to develop expertise in, Risk-Weighted Assets, regulatory capital frameworks, and credit risk concepts including PD, LGD, EAD, Definition of Default, and the application, monitoring, and reporting of Standardised and Internal Ratings-Based credit risk approaches.
  • Interest in financial markets, banking products, balance sheets, financial reporting, and risk management.
  • Responsible, driven, detail-oriented, and focused on data quality and delivery.
  • Strong communication and problem-solving skills, with the ability to collaborate effectively across business and risk functions.

Benefits

  • Gross monthly salary based on a 36-hour work week, including holiday allowance and a flexible benefit budget.
  • Pension scheme.
  • Flexible hybrid working and an ergonomic home office setup.
  • Five weeks of vacation per year, supplemented by two mandatory days off, with the option to purchase up to four additional weeks annually.
  • Five additional days off for personal development or volunteer work.
  • Development budget of €1,000 per year, accumulating up to €3,000.
  • Annual public transport pass providing free public transportation throughout the Netherlands for business and private use.

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