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Hi, my name is Koen, 26 years old and raised in Enkhuizen but since April I live in Amsterdam-Oost.
In my spare time (and in between work) I’m busy with sports by working out five times a week and watching weekly soccer (Feyenoord) and Formula 1.
In recent years I have been fairly active on “Untappd,” a social medium focused on specialty beer.
This has gotten a bit out of hand so I try to find new special beers every week.
Besides this, I try to go on a far away trip at least once a year because I like to see more of the world!
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Where did you work before joining Data Science Lab?
I have been working at DSL since early 2018, prior to that I worked at Grant Thornton as a data scientist for six months.
I ended up here to do my graduation project for my Master Business Analytics at the VU.
I followed this master prior to the Bachelor Business Analytics of the same name.
During my studies, I worked in the kitchen of a restaurant for over five years.
This made me enjoy cooking.
During this period, I also attended a Minor in the southern U.S., which always makes me look at news coverage about this country with a little extra attention, especially during such troubled times as now.
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What are the duties as a ‘Data Scientist’ at Data Science Lab and what do you personally find most interesting about it?
As a data scientist at DSL, I’m certainly not just “code knocking” as some people might expect.
Alongside this, we are certainly also consultants, which means, among other things, that I have regular discussions with clients to get clear on how data science can help with operations.
In addition to creating effective data science solutions, I regularly participate in brainstorming and inspiration sessions, help draw up project proposals, and hold many client meetings to ensure that the work we do for clients runs smoothly from start to finish.
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Within your current project, what is the biggest technical challenge?
I am currently working on two different projects with various challenges.
For The Buddy House we are working on an app in which many different aspects come together: app development, cloud services, data science models, CICD and my role as ‘scrum master’ in this project.
A beautiful and unique project in which a number of things are new to me, at the same time making it challenging and very educational!
In addition, I am currently working on a voice interactive chatbot for PGGM.
The challenge here is to connect the solution we have already developed to the internal systems.
This with a view to testing the solution with hopefully a final deployment as the end result.
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What do you think is the biggest misconception of data science?
I am sometimes bothered by the noises about data science & AI in the media where it is implied that computers will make decisions for us.
Often such noises come from people without an AI background where they present situations from the movie “I, Robot” as a future perspective, for example.
Here people see data science as computer models in which decisions are made without human controls.
This is obviously not how data science is efficiently deployed; a data science solution must be supportive of people working within a process.
These solutions must make work easier and/or provide more insights where the decision-making power always remains with humans.
After all, a human being remains much better at interpreting results, no matter how well a computer can calculate.
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How do you see data science in 10 years?
I think the developments that are already visible now will continue.
I am talking about solutions offered by the big tech companies that can be pulled right off the shelf.
There are already many packages and software solutions available from Google, Amazon, Microsoft, etc. that are applicable in areas such as NLP, voice-to-text and neural networks.
These offerings are getting better and better and I expect this trend to continue.
I therefore expect that in the near future more and more can be built upon existing solutions, with the exception of very complex or unique issues.
My work will then mainly focus on getting the customer demand in focus, tuning existing solutions for specific issues and making the solution available to the end user within a user-friendly format.
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What problem would you ever want to solve through data science?
I think it would be cool to be able to provide complete insight into the development of an athlete.
From nutrition to training.
That everything of influence can be seen in the data.
That the influence of nutrition scheme X or training program Y can be seen exactly on the top speed of Mathieu van der Poel or the impact of Rico Verhoeven’s right direct.
Another interesting project that is certainly feasible; being able to “predict” the ideal beer based on someone’s previous reviews using data from Untappd.
Or perhaps one step further, to be able to determine someone’s ideal beer and brew it yourself.
Perhaps a fun future project for our Lab day!
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