Data Quality Professional ING’s purpose is “Empowering people to stay a step ahead in life and in business”. Every ING colleague is given the opportunity to contribute to that vision. The Data Quality Professional for PD Remediation Squad will support in data collection, data analyses, and data remediation for Wholesale Banking Domain. They will be working in an environment with highly experienced professionals, and different stakeholders like data owners, data stewards, operational teams, IT engineers and other functions within the bank. They will drive the analysis of Data Quality issues and propose remediation actions within the bank, as well as contribute to build a culture of continuous improvement in Data Quality.

If you can:

• Conduct thorough analysis and articulation of credit risk model data requirements, identification and understanding of specific data quality issues to ensure effective remediation and delivery of the data to the users.

• Perform data quality checks on the critical data elements for model data and identify the main data quality issues that require remediation.

• Perform remediation of the data based on agreed requirements and deliver the remediated data to end users.

• Perform root cause analysis on prioritized Data Quality issues to determine appropriate fix forward, fix backward actions or remediation plans.

• Collaborate and communicate with partners/stakeholders within the Wholesale Banking domain (Modelling, Data, Front Office representatives in the local business units across the globe and many more).

• Work with key stakeholders to align expected turnaround times for remediation, gain approval for suggested remediation actions, coordinate the appropriate action with the teams that are responsible for executing those actions and monitor remediation actions till closure.

If you are a Professional who possesses the following:

• Experience in performing data analyses using SAS Enterprise Guide (or SAS Base).

• At least 3 years of experience in a data analysis-oriented function.

• An analytical, curious, and “challenger” mindset.

• Knowledge on Data Quality Management practices such as Data Remediation, Data Profiling, DQ Assessment, Data Cleansing, Root-Cause analysis, etc.

• Assertive and open to feedback and ideas from others. • Flexible and easy in communicating complex topics and solutions to varying audience (i.e. data users, systems owners and Front Office/Relationship Managers).

• Knowledge and understanding on the flow of data is an advantage.

• Background or knowledge on bank regulations, credit risk, risk model data is an advantage.