Data Science
Data must be given meaning. We do this within Data Science: statistics for advanced users with the aim of interpreting the data and drawing valuable conclusions from it.
At Data Science we solve the most complex problems with algorithms. We build and validate models to cover risks and optimise processes. We use them to create new or improved products and services. And with which we remain a solid bank that complies with the relevant laws and regulations.
Using data as the key to fighting financial crime
Can you fight financial crime with data? It can be done, as long as you know what numbers to look at. This is the biggest challenge for Business Intelligence Translator Tim, who explained why data-driven work is so important: “Data gives meaning to numbers.”
Jobs in Data & Analytics
View all open vacancies and apply!
- Markets Global Communications Advisor
- Hybrid
- Utrecht (NL)
- €4,931 - €7,043 (scale 09)
- 40 hours
- Full time
- RPA & Low-Code Engineer
- Hybrid
- Utrecht (NL)
- €3,447 - €4,923 (scale 07)
- 36 hours
- Full time
- Salesforce Solution Architect
- Hybrid
- Utrecht (NL)
- €5,876 - €8,395 (scale 10)
- 36 hours
- Full time
- NZ CBNZ Client Due Diligence Manager
- On site
- Hamilton (NZ)
- 37.5 hours
- Full time
- Agribusiness Manager
- On site
- Dargaville (NZ)
- 37.5 hours
- Full time
FAQ
Data Science is responsible for the development of (applied) analytics solutions based on large data sets. The common denominator of all the solutions we build, is that we try to utilize big data sources to create business value. In order to do that, we need to know what the business needs, analyse the data and translate business needs to a suitable analytics solution.
There is a lot of information in data. Utilising this information can further improve the clients experience, but it can also streamline internal processes and contribute to more revenue.
Data scientists in finance mostly work on applied analytics solutions, meaning that the solutions data scientists build end up in production. In order to do this data scientists need to create robust data pipelines and develop models in such a way that they can be put into production. Typical data science puzzles in finance are about knowing your customers, risk detection and natural language processing.
At Rabobank, we use a wide variety of analytics techniques, depending on the use case and the available data. Examples are Logistical regressions, Monte Carlo simulations, Autoregressive econometrical models and Machine Learning techniques, but this is just the tip of the iceberg.
Data validation is the set of tools and methods used to assess the data supporting the risk measurement and management process. Specifically, we verify the following aspects: data collection process, data quality, data treatment as well as the existence of policies and rules regulating the whole data life cycle, from data entry to reporting. We do this for both historical data and current application data.