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Senior Data Analyst Credit Risk Modelling

4 weken geleden


Amsterdam Metropolitan Area, Nederland Ayvens Voltijd

You are responsible for:


For our Senior Data Analyst Credit Risk Modelling vacancy we are looking for a new colleague. The responsibilities of the team are focused on assessing and reporting of the performance of credit-risk rating systems. As the team is involved in all aspects of the rating system life cycle, you will work together with model developers, validators, and the business on analyzing data, mapping business requirements and creating data marts for downstream consumption. Finally, you will be estimating the impact of changes in the data landscape on risk calculations (TREA, provisions)


Our ideal candidate is working with an outperforming drive and has an analysing and problem-solving mentality. You are responsible for:


  • Perform thorough analysis and articulation of Credit Risk model data requirements, identification and understanding of specific data quality issues to ensure effective delivery data to the model development team;
  • Handle and maintain extensive databases within the banking sector, focusing on Credit Risk data;
  • Apply advanced data warehouse concepts and data modeling techniques to ensure robust data architecture;
  • Utilize advanced knowledge of Credit Risk metrics (PD, LGD, RWA, EL) to support risk assessment and reporting;
  • Translate business requirements and observed defects into clear user stories;
  • Identify and define new data improvement opportunities. authority levels, discount levels, limit of self-retention limits, inception and renewal procedures.



To succeed in this role, you have the following skills and experience:


The ideal candidate has the interpersonal skills necessary to manage and build relationships across multiple departments. Collaboration, agility project management and decision making are examples of key behaviours reflecting the agile culture of Ayvens.


  • Approx. 7+ years of related work experience (credit risk measurement and/or modelling data management);
  • A university degree in Statistics, Mathematics, Computer Science, Engineering, or similar analytical field preferred;
  • Advanced level of BI tools such as SQL Developer and Python;
  • Experience in working with large databases in the banking industry, preferably Credit Risk related;
  • You have experience in script optimization in SQL or Python;
  • You are able to work with database engine and reporting tools;
  • Ability to interact with stakeholders at all levels and to work with different cultures and nationalities.




Why Ayvens?


Subsidiary of the Société Générale group, specialized in the automobile financial services, Ayvens, born from the combination of ALD Automotive and LeasePlan, is the leader of the Operational car leasing. Its range of services allows international companies to concentrate on their business line by delegating the financing, the management and the maintenance of their car fleet.


The financial holding company, AyvensS.A, is a regulated and listed institution and is responsible for the development of the group and of the supervision of its activities in 44 countries where it is implanted. It brings a technical support for subsidiaries thanks to its role of coordination and favors the creation of synergies between countries and regions.


Customers looking for partnership expect from their lessor to act as a trusted advisor to help them make the right fleet management and sustainable mobility choices. This need is booming in a context of stronger relationship commitments from our customers and car industry fast changes.

Join us on this exciting journey as we continue to enable the transformation towards large scale adoption of sustainable mobility and provide our customers with the solutions they need to succeed. Follow our page for the latest updates, news, and insights.






Join us


If you have the right profile and experience and would like to join Ayvens on its journey, do let us know why you are our new Senior Data Analyst Credit Risk Modelling.