Credit Risk Model Data Analyst – ING Hubs Poland
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Qualifications
- Experience in working in the area of financial or credit risk.
- SQL very well.
- Programming very well in SAS (4GL).
- Interpret data on your own based on the collected information and assumptions.
- Ability to clearly and succinctly express ideas, facts and opinions and express them fluently both in speaking and writing (also in English language), supported by appropriate tools.
- Know how to identify problems, analyzing key information and making connections, in order to find appropriate solutions.
- Complete tasks and achieve results in an efficient, timely and high-quality manner, with a focus on execution and delivery of targets and KPIs.
Additional Skills for Extra Points
- Understanding of the credit process at the Bank.
- Experience in working with credit or market risk modeling (PD, LGD, EAD, IFRS9, IRB).
- Experience in working with Agile / Scrum.
- Advanced analytical skills.
- Independent, creative and proactive way of thinking.
- Ability to question the status quo.
- Knowledge of techniques used in business analysis.
- GIT.
- Azure DevOps.
- Confluence.
Your Responsibilities
- Preparing data for risk modeling.
- Data quality assessment and root cause analysis.
- Analysis of internal and external regulations and preparation of functional documentation on this basis.
- Data analysis and preparation of reports.
Information About The Squad
We are looking for specialists / experts to join the Wholesale Banking Credit Risk Model Data team to support the processes of building datasets for risk models and management reports in the area of finance and risk. We combine knowledge of risk modeling and advanced data processing. We have extensive knowledge of the Banks processes, which enables us to understand and interpret data for the purposes of modeling and reporting the phenomena accompanying these processes. We work in an international environment, we use English on a daily basis (written and spoken). Our work focuses on data analysis and preparing it for reporting and modeling.
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