Communication: the life of a bol.com Data Scientist part 2

4 min.
18 February 2019

Data science is hot! Changing your job title to data scientist on LinkedIn will trigger a small army of recruiters to swarm you with all kinds of awesome job offers. So in terms of career prospective jumping on the data science hype train seems like a good bet. But what skills do you need in order to be a good data scientist? No wait, let’s rephrase that question: what is data science in the first place?

This might seem like a silly question but if we dig a little deeper we find that data science actually does not always mean the same thing. If you ask different people it can be anything from market research to hardcore artificial intelligence. Although both of these extremes build on utilizing data, the skills needed for either discipline vary wildly.

So what does data science mean in the context of bol.com? This series of blog posts is meant to shed some light on what we, the data scientists of bol.com, think data science is and what a good data scientist should be able to do. We have identified four key aspects that should be central in the skillset of every bol.com data scientist and asked some of our team members how these aspects feature in their day-to-day work.

Communication

Our first topic of discussion is communication. Now why would communication be essential to being a data scientist when you are able to create wonderful models that can predict all kinds of essential things? Our team members explain.

Wesley Verheul

Data Science is teamwork. I doubt there is one data scientist that is an expert on everything and if they do exist they are about as rare as unicorns. To me it’s about leveraging and coordinating different kinds of expertise, both in- and outside your team, in order to come up with a valuable solution to our customers’ problems. You need to be able to communicate proactively with your peers, be able to explain what you are doing and why you are doing it to people who do not have the same technical background as you do, and finally bring people on board with your vision.

Joep Janssen

As a Data Scientist, being a linking-pin is the norm. Having in-depth discussions with fellow engineers will make you a better engineer and will allow you to attack a problem more efficiently. Also, you need to explain your choices and why you made them to almost anybody involved. It helps if you enjoy introducing laymen into a discipline that is often compared to ‘a bucket of gold-dust’ or ‘the new bacon’ and make them understand that its main purpose is to bring method to the madness of today’s data-driven businesses.

Ernst Kuiper

Data Science and Machine Learning are relatively ‘new’ disciplines. This is also the case within bol.com. Our team in its current incarnation has been formed less than a year ago and it has been growing rapidly because our directors see the potential in leveraging the predictive power of Data Science. This, however, does not mean that the rest of bol.com is on board as well. This can be due to distrust as mentioned by the others, but a lot of people also just don’t know what to expect from it or how they can use Data Science to their advantage in their particular corner of bol.com. It is therefore up to us to educate and enthuse our colleagues and show off the power of not only machine learning but also working in a data-driven way. This means actively taking the stage in front of 1500 colleagues to celebrate our awesome achievements. Scary? Yeah definitely. But seeing colleagues realize the potential of what we are doing is definitely worth it!

Asparuh Hristov

Even after you come up with a solution and make the service available for use, you still haven’t created an impact if nobody is using it. You would need to convince the users that your application is reliable and that it is better than what they have always used. Convince them to make a change and step into the unknown. Good luck with that if you cannot explain in a clear manner. Presentation skills can make the difference between your work being forgotten and never used and being the most valuable contribution to the company. People are naturally afraid of unknowns and “black-boxes”, therefore it will take extra effort from your side and you would have to explain highly technical details in layman terms.

Loïs Mooiman

For a lot of people Data Science is magic. Magic is however unexplainable and thus people do not trust it, which results in nobody using the services that you made. This is a shame because magic can also be very wonderful and useful. So, you will need to explain and convince people of your ideas or to even use your services. To do this you will need the difficult skill of explaining hard problems in a clear and simple manner. Make your 4-year-old niece understand what kind of awesome things you are doing and you are ready to go. Also when people understand what you are doing then they can provide very relevant feedback for version 2.0.