After my master’s in data science, business and governance, I started work at Nationale-Nederlanden on a data science traineeship via an external agency. It was a conscious decision to stay. Nationale-Nederlanden has access to lots of data and the technical infrastructure to produce and implement predictive models. That’s why Nationale-Nederlanden is a great place to work for data scientists. I also work with a team of experienced data scientists, who all bring their own expertise to the table.

Basically, all our insurance products can be traced back to data. Added to this, Nationale-Nederlanden has been around for a long time, so we also have lots of data from the past. All this data is crucially important to us to be able to market good, competitive propositions.

I work in two-week sprints with my scrum team. We have a stand-up meeting two to three times a week. It’s a great way of working: I know exactly what I have to do and know precisely what my colleagues are up to. It makes it easy to help each other.

I build models in the R and Python programming languages. I’m currently working on a model that identifies customers who only have one policy via an adviser and who have the highest chance of taking out another policy. This is how we promote cross-selling and try to solve the problem that many advisers in the Netherlands have customer relationships based on a single policy, whereas advisers would of course like to build a full-service relationship with their customers. This is how we can deliver added value for our customers and therefore be more relevant to them.

In general, Nationale-Nederlanden relies on data more and more. When I first started here, you encountered it here and there in the organisation, but now you can see that data is being used more and more and that lots more decisions are made based on the insights that data delivers.

I really feel at home here. Our company is informal and accessible. It’s inspiring to me that my colleagues like to get stuck into their work and enjoy putting things in place. Finally, I’m given all the scope I need to develop my data science skills further, for example, if I start a project using new technology.

 

Vera de Jong
Data Scientist Customer & Commerce