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9 hours ago
We are looking for a skilled Data Engineer to join our Non-Retail Credit Risk modelling team. As a key member of this team, you will be responsible for developing credit risk models, working with large datasets, and collaborating with stakeholders across the bank.
This role offers an exciting mix of challenges, including data management, analysis, and visualization. You will have the opportunity to work on complex projects, develop your skills in data-driven insights, and contribute to the growth of the organization.
About the JobYou will be responsible for managing and analyzing structured data, working with large datasets, and providing data-driven insights to stakeholders. You will also develop credit risk models, using techniques such as PD, LGD, and EAD, to support business decisions.
As a member of this team, you will have the opportunity to work on complex projects, develop your skills in data-driven insights, and contribute to the growth of the organization.
Responsibilities- Data Management and Analysis: You will be responsible for managing and analyzing structured data, working with large datasets, and providing data-driven insights to stakeholders.
- Credit Risk Modelling: You will develop credit risk models, using techniques such as PD, LGD, and EAD, to support business decisions.
- Team Collaboration: You will work closely with the Non-Retail Credit Risk modelling team, as well as other teams across the bank, to ensure effective communication and collaboration.
- Problem-Solving: You will identify data deficiencies and contribute to designing solutions to address these issues.
- Master's Degree: A Master's degree in a quantitative field, such as statistics, mathematics, finance, accounting, economics, or engineering.
- Analytical and Technical Skills: Strong analytical and technical skills to work with large and complex data sets.
- Experience: Experience from responsibilities relating to control and analysis processes, data and business intelligence, knowledge of statistical models, and hands-on experience with extraction and visualization of insights from large datasets.
- Programming Languages: Knowledge of Python, with experience with other languages such as SAS and SQL being an advantage.
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