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Hi, I’m Sidney, 24 years old and data scientist at Data Science Lab.
My current project is at Fokker Services, a remnant of the former Fokker Aircraft, where I have had the opportunity to work on various projects for over a year now.
Fokker maintains, modifies and repairs aircraft and combat aircraft of various air forces.
Besides Fokker, I work on Lab days on various internal projects, which are very educational in addition to developing our organization.
Besides work, I enjoy programming on personal hobby projects.
I also like to be in the kitchen experimenting with new recipes.
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Where did you work before joining Data Science Lab?
I completed the Master Data Science and Bachelor Information Science at the UvA.
During the Master I did my graduation project at the startup Grasple.
They run an online platform where students learn mathematics and statistics through assignments.
My project consisted of developing a model that approved or rejected answers to open-ended questions.
After this Masters, I also worked there as a data scientist for six months before joining Data Science Lab.
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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?
My work as a data scientist is very diverse: in addition to developing models, I work on analyses and visualizations, interactive dashboards, APIs, as well as data engineering.
Personally, I find developing models the most interesting challenge, because this is one big puzzle of data and parameters.
To eventually be able to solve such a puzzle and thus contribute to an organization gives me great satisfaction.
Besides all these ‘technical’ skills, the social aspects and business consultancy are also very important to me.
You are more than just a code monkey, you also have to be able to communicate well with the end users and convert the needs, wishes and goals into a working solution.
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Within your current project, what is the biggest technical challenge?
In my current projects, the biggest challenge is data availability and quality.
The original data structure was not set up for the idea of a data science department as a user.
Fortunately, we are gradually realizing, through realistic small steps, our own environment where we can add and remove everything ourselves.
Despite these steps, there are still amounts of important data that are not yet centralized because they are in PDF, Excel, or access files, for example.
Fortunately, there are nice advanced analytics techniques for this to convert this data into useful qualitative data.
This is what we will be working on in the near future.
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What do you think is the biggest misconception of data science?
A major misconception is that many people view data science as a magical “black box” that can solve every problem without error.
Often the data needed is not considered, and a model is not trusted if it does not make 100% correct predictions.
This while of course the people who first made the predictions also made mistakes and the model does end up doing better.
It is therefore an important task to remove all doubts, questions and the mystique of the model, because in the end you do need the trust of your users.
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How do you see data science in 10 years?
I think that in 10 years a data scientist will have almost no need to do data processing and probably no need to tinker with models themselves.
AutoML platforms will do this work automatically.
As a result, the work of a data scientist will become increasingly human-driven in the form of defining a problem and interpreting the output of the automatically generated model.
Although you are already the bridge between the “magic” model and the organization, this role will only increase.
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What problem would you ever want to solve through data science?
I think it would be very nice to work on a solution against food waste and especially at the supermarket and production level.
A lot of food still ends up in the container because too much is produced or purchased.
It would be cool if a model could predict the demand for products in such a way that as little needs to be thrown away as possible, but of course also without emptying the shelves.
Although this is an enormously difficult task, I think it would be interesting to use data science to solve social problems and make the world a little better.
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