Interview with a Data Scientist (Jeroen Latour)

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I interviewed a former work colleague – Jeroen Latour. Jeroen is a Senior Data Scientist with a consultancy in the Netherlands called TriFinance and he will soon be joining the Booking.com team as a Data Scientist. He remarks on his analytics consultancy experience here which was formed both at IBM and TriFinance.

1. What project have you worked on do you wish you could go back to, and do better?

Most of them. The downside of analytics consultancy is that often you’re not able to continue on a promising project. Fact is, the challenge of introducing data science to an organisation is often not a technical one, and it takes more time to take people along than it does to do the analysis. With a little patience, sometimes you get to continue and fulfill the promise.
2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
For analytics professionals, I’d say: get as much real-world experience as possible. A lot of the competitions provide nicely prepared datasets, predefined challenges and in particular very little restrictions on how your work should be usable by others. That makes them quite a different experience from doing data science inside an organisation, where you have to work with people to get clarity on the question, spend lots of time trying to understand and clean up the data you got, and you have to either make your work easy to interpret by people outside your team, or your work has to technically fit inside the client’s infrastructure.
If you don’t currently have a chance to work with clients, you can build experience by doing data volunteer work, such as with the many Data for Good groups.
As for the PhD students, I can only say: use this time to learn as much as you can about research methodology. No matter whether you stay in academics or move to the corporate world, it will help tremendously in approaching problems with an analytical mind.
3. What do you wish you knew earlier about being a data scientist?
That I like to be one, or that this job exists. While my academic background is quite relevant to my current work, it took a few years in ‘regular’ IT work before I ended up in analytics.
4. How do you respond when you hear the phrase ‘big data’?
Smile and nod. No matter how you feel about the phrase – and the seeming disconnect between it’s ‘official’ meaning and the way it’s being used – it has done a great deal to make organisations think about how they can use their data to make smart decisions, and that makes it a lot easier to get on the agenda and build enthusiasm. So, just keep smiling,
5. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 
Educate, and focus on the ROI. To many people, it’s not quite clear what a data science project entails (‘but, what do you…. do?’) so it’s very important to make it a collaboration, make transparent what is needed to make it work (data cleaning, anyone?) and connect it to the end goal. The goal, or the Return on Investment (ROI) is also what defines whether your work is good enough. Are you going to get enough out of the next analysis, to warrant starting it? Don’t just focus on ‘insight’, focus on what you can do with it.
6. What are the differences between Software Development, BI and Data Science for you?
Three fields that are more related than some are willing to admit. I’m not so adamant on distinguishing Data Science from BI, both are trying to see the world through data, with perhaps Data Science being more free-form and using more advanced tools, while BI is focused more on repeatability and making analysis available to end users.
7. How do you explain the importance of Analytics to skeptical management? 
Again, focus on the goals. What can you do that is important to them, what issues can you help solve that they worry about, what benefit can you bring that is valuable to them.
8. What is the most exciting thing about your field?
Let me name two things. First of all, it’s a field in which you work with all kinds of people, and get a much broader view than if you would only focus on data or IT.
Second of all, the field is developing at an amazing pace. The teams are getting more professional, the tools more powerful and effective, and we’re learning more and more about building data-driven organisations. That’s super-exciting to be a part of.
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