Trainee @ ING | Interview with Nikki van Ommeren

Siza Toro
01 april 2019

Nikki van Ommeren, IT trainee at ING

Q: Tell us something about you, who you are and what are you currently doing?
A: My name is Nikki and I studied Econometrics at the UvA, both bachelors and masters. I started in 2010, finished my bachelors in 4 years and my masters in the following year. I am also not unfamiliar with the VSAE since I was active in a lot of committees during my study period. For example, I was in the party committee, almanac, lustrum, Econometric Game and the last one was the ISP. At the moment I’m working as an IT trainee for, unlike most of my fellow econometricians who end up doing the risk traineeship. That is because I like the programming side of econometrics a lot. So, I did the minor Programming and I started tutoring at the UvA. During this period, I became more and more passionate about programming and IT, so after my masters in econometrics I pursued a master’s in software engineering.

Q: How did you end up at the traineeship of ING?
A: I ended up at ING’s traineeship because I was looking for a job where I could combine programming and data science and where one can learn and develop a lot. In that respect ING had, or I might even say has, the perfect traineeship.

Q: Does your current work at ING meet the expectations you had?
A: Yes, definitely! The way you shape your work at ING is completely up to you. Once you start your traineeship at ING, they will help you find the right department to work at and for me one of the options was to work at a start-up of ING. This start-up of ING is an app called Yolt. This is where I currently work on and I am part of the data science/data engineering team. We are responsible for the models behind the app and for making the models applicable. In addition, we also work as data analyst where we handle questions from the business. I like this combination of work because on the business side there are a couple more deadlines, but the results are faster visible. On the other hand, building models has more content but it may take more time to see results.

Q: Can you give an example of a model that you applied?
A: Yes, but firstly I have to explain what Yolt is. Yolt is an app that gives an overview of all your bank accounts, even if you have different accounts at different banks. It can also categorize expenditures. For example if you want to keep track of how much you spend on lunch, you can track your monthly expenditure in Yolt. One of the models that we apply relates to categorizing payments. It applies text recognition to recognize that a transaction is lunch and not for example a haircut at the barbershop.

Q: So how does Yolt make use of machine learning?
A: We use machine learning in the earlier mentioned model to categorize transactions.

Q: Would you say that the main goal of Yolt is to categorize transactions or rather give a total balance of all the different bank accounts users have?
A: Well it’s funny you are asking me this because we see that there are different types of users within Yolt. Some use Yolt as a bank aggregator and others use the app to label their payments and transactions. Our main goal is to help people to manage their money more efficiently and provide them with the insights how to do so.

Q: Do you feel like your master was in line with the current work you do?
A: To be honest, for a large part it is not. It would be more in line with the course Machine learning, but that was not part of any of the specializations when I graduated. Back then some of the courses were a little bit too theoretical to be directly applicable to the working environment, with questions like “show that this holds” or “prove…”.

Q: Which programming languages do you guys currently work with?
A: I work mostly with Python and Pyspark and a little of SQL. And a part of the team uses Scala.

Q: Last question, which tips would you give to the young econometricians?
A: Well I recommend students, especially those in the last phase of the bachelor, to gain some working experience to find out what you really like before starting working fulltime. This could be by something like the Learning Academy or an internship between the bachelor and the master. I only started with an internship after my master’s, but I think working experience would have greatly helped me in choosing a master specialization.