Mid-Level, Senior
Help Shape Better Decisions Through Data
At ING, we believe data has the power to simplify complexity and help people stay a step ahead in life and business. We're looking for a Data Analyst (Credit Risk) who enjoys solving complex data challenges, uncovering insights, and helping drive better risk decisions across a global banking environment.
If you're passionate about data, thrive in collaborative teams, and enjoy turning information into meaningful outcomes, we'd love to hear from you.
Role Summary
As a Data Analyst (Credit Risk), you will support credit risk model monitoring and validation activities through data analysis, data collection, and data quality management. You'll work with large and complex datasets from multiple source systems, transforming them into reliable data assets that enable risk modelling teams to assess model performance and support important business decisions.
You'll collaborate with a diverse group of stakeholders across the bank while contributing to ING's global data management strategy and agile ways of working.
About the Team
You will join our Data Analytics team, a group of specialists supporting risk modelling and management reporting across Finance and Risk. Combining expertise in risk analytics, banking processes, and advanced data processing, the team plays a critical role in ensuring high-quality data is available for modelling, reporting, and decision-making.
What You'll Be Doing
Analyze and translate credit risk model data requirements into effective data solutions
Develop and maintain SAS programs to support data collection activities
Identify, investigate, and resolve data quality issues
Create and maintain technical documentation for end-to-end data processes
Deliver high-quality data marts and datasets to model development and validation teams
Recommend and implement improvements to data collection and processing practices
Partner with stakeholders to understand evolving data and reporting needs
What We're Looking For
Required Skills
Degree in Mathematics, Statistics, Computer Science, Data Science, Information Management, or a related field
Experience in data analysis within banking, finance, or credit risk environments
Strong SQL skills
Strong SAS (4GL) programming skills
Ability to analyze complex datasets and draw meaningful conclusions
Strong problem-solving and communication skills
High attention to detail and commitment to data quality
Ability to manage priorities and deliver results in a fast-paced environment
Preferred Skills
Understanding of banking credit processes
Experience with credit risk modelling concepts such as PD, LGD, EAD, IFRS9, IRB, and Definition of Default
Knowledge of bank balance sheets and financial reporting
Experience working in Agile/Scrum environments
Familiarity with Bash, Git/GitLab, Jenkins, and Confluence
What We Offer
A collaborative and inclusive international environment
Opportunities to work on complex risk and analytics challenges
Continuous learning and professional development
Exposure to global stakeholders and banking processes
The chance to contribute to meaningful decisions that help customers and businesses move forward
How to Apply
Click the application button on our career site and submit your application through Workday. We look forward to learning more about you.
Equal Opportunity
Don't meet every single requirement? Apply anyway. Research shows that many talented candidates only apply when they meet all qualifications. If this role excites you, we'd love to hear from you.
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About ING
With 65,000 employees and operations in approximately 40 countries, there is no shortage of opportunities for people with initiative who want to help people take a step ahead in life and in business. Do you want to work at the cutting edge of what's possible and at the same time ensure you work with integrity and hold the customer's interests at heart? Do you want to be surrounded by progressive, inspiring, diverse, and supportive colleagues? Then there is no better place to invest your talents than at ING. Join us. Apply today.
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