- Erick Webbe is the Head of Data Science at bol.com, the biggest online retailer in northwestern Europe.
- Erick's background in physics forms a basis for his philosophy on life and work, which he applies to his work every single day.
- Experimentation and critical thinking are key skills for data scientists, and Erick recommends applying them every day.
- To solve problems effectively, it's important to keep the end goal in mind and focus on achieving it.
- Erick recommends adding a generalist to a problem if you don't know where to begin, as they can bring the problem from the original challenge to the first sixty percent solution before adding more specialized people to it.
My guest this week is Erick Webbe, Head of Data Science at bol.com. Bol.com is the biggest online retailer in northwestern Europe, serving about 12 million customers, as a general retailer similar to Amazon.com.
Erick has a Master’s degree in Applied Physics. His background in physics forms a basis for his philosophy on life and work. That’s a “philosophy that I still apply to my work every single day […] we think about how we can best help them overcome that problem or solve it, and then test that in real life as soon as we can.” Applying this mindset to data scientists, he continues, “every data scientist will be familiar with experimentation. Experimentation is your bread and butter there, so that’s something you apply every single day. But also the critical mindset, and being able to separate main contributions to smaller contributions, which I think is a key part of an applied physics background is something that still is very useful.”
In creating solutions and solving problems, Erick recommends, “It’s not about building the most beautiful trap or the most elaborate way to catch it, you catch it because you’re hungry. If you always keep the end goal in mind and then start to think about how can I achieve that, that’s when you’ll? That way we’ll become most effective.” Following that idea of solving problems effectively, he recommends “that’s the way to build credibility, and to build a reputation for yourself of being someone that can actually solve a problem, that you can then always leverage on a later point in time for the more fancy stuff, for the more modeling stuff.”
He has a unique recommendation that I haven’t heard before about how to start dealing with a problem. “If you have a problem and you don’t know where to begin at all, it’s also very useful to add a generalist to it. Someone that knows a lot from a lot of different things a little bit, and to move it one stage further in our understanding before adding any more specialized people to it.” The generalist will hit a point where specialists need to get involved. “The generalist that can bring the problem from the original challenge to the first sixty percent solution, and after that you should surely add more specialists to bring it a lot further.”
Check out the episode to hear even more of Erick’s thoughts on how to deal with difficult data engineering problems for data science projects, his personal philosophy of treating his career as an experiment with evaluations, and how he prepared to start leading a data science team. There are so many great nuggets in this interview, and you won’t want to miss it.