- Erick Webb, Head of Data Science at bol.com, emphasizes the importance of experimentation in data science and problem-solving.
- Webb suggests focusing on solving problems effectively rather than pursuing the most elaborate solutions.
- Building credibility by consistently solving problems is crucial for establishing a reputation in data science.
- Webb recommends starting problem-solving by adding a generalist to gain a broad understanding before involving specialized experts.
- The generalist should lead the problem-solving process up to a certain point before specialists are brought in for further refinement.
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.
Frequently Asked Questions (AI FAQ by Summarizes)What does Erick Webb, Head of Data Science at bol.com, emphasize in data science and problem-solving?
Erick Webb emphasizes the importance of experimentation and effective problem-solving in data science.
What does Webb suggest regarding solving problems effectively?
Webb suggests focusing on solving problems effectively rather than pursuing the most elaborate solutions.
Why is building credibility through consistently solving problems crucial in data science?
Building credibility by consistently solving problems is crucial for establishing a reputation in data science.
What does Webb recommend as a starting point for problem-solving?
Webb recommends starting problem-solving by adding a generalist to gain a broad understanding before involving specialized experts.
When should specialists be brought in during the problem-solving process according to Webb?
The generalist should lead the problem-solving process up to a certain point before specialists are brought in for further refinement.
How does Webb's background in physics influence his approach to work?
Webb's background in physics influences his approach to work by emphasizing critical thinking and separating main contributions from smaller ones.
What is key for data scientists according to Webb?
Applying a mindset of continuous experimentation is key for data scientists, according to Webb.
What leads to effective problem-solving according to Webb?
Keeping the end goal in mind and strategizing on how to achieve it leads to effective problem-solving.
How does Webb guide his leadership in data science?
Webb's philosophy of treating his career as an experiment with evaluations guides his leadership in data science.
How can adding a generalist before involving specialists enhance problem-solving efficiency?
Webb's unique recommendation of adding a generalist to tackle complex problems before involving specialists can enhance problem-solving efficiency.
