Data Analyst

at  ING

We are looking for you, if you:

• have Bachelor’s or Master’s degree in Information Management, Econometrics, Computer Sciences, or alike, and/or equivalent,
• have at least 5 years of professional experience within data analysis,
• have working experience in a bank or financial institution,
• have broad knowledge of data management, relation databases, data modelling,
• have hands-on working experience with SQL,
• have hands-on working experience in SAS environment (SAS Enterprise Guide) and 4GL programming,
• are strong collaborative team player, working within and across multiple virtual teams,
• have the ability to effectively communicate complex technical content to business/functional audiences,
• have curious and analytical mindset,
• are eager to learn more and proactively acquire knowledge,
• spoken and written English - C1

You’ll get extra points for:

• experience with credit risk model development and/or validation,
• knowledge about credit risk models (PD, LGD, EAD, IFRS9, IRRBB),
• experience in working with Agile / Scrum,
• hands on experience in VBA.

Your responsibilities:

• performing data analytical activities: sources investigation, reports preparation, creation of dashboards, automations,
• identify opportunities to automate and optimize processes,
• performing analysis to assess quality and meaning of data,
• analysis of internal and external regulations,
• functional documentation preparation.

Information about the squad:
We are looking for specialists to join Data Labs team to support data management and risk reporting. The team sifts through the complexity of credit risk data to identify, construct and implement data strategies, optimised processes, and standard approaches to address data demands across the bank.
The role naming convention in the global ING job architecture will be “Data Scientist III”

• performing data analytical activities: sources investigation, reports preparation, creation of dashboards, automations,
• identify opportunities to automate and optimize processes,
• performing analysis to assess quality and meaning of data,
• analysis of internal and external regulations,
• functional documentation preparation