Amadeus works hard to integrate data science into its teams.
Amadeus is one of the world’s largest travel technology services, with thousands of clients in the travel industry. In 2013, it stepped up its game by creating an analytics-based travel intelligence unit that has hired more than 40 data scientists — a brand new position at the company.
Finding this kind of talent at a time when many companies would like just one or two data scientists presented an obvious challenge; Amadeus had to get creative. The company pulled some of its data scientists from its operations research group. The rest it hired from outside the company, taking advantage of ties it had with universities, especially in Europe.
But hiring was just the starting point of a process that integrated data science talent into the company’s organizational structure. MIT Sloan Management Review’s Michael Fitzgerald talked with Denis Arnaud, the company’s applied research senior manager, about the process.
Data scientist is a new job title at Amadeus. How did you change your interview process for it?
The process was not different now than what it was 10 years ago, for management and operations research for airlines. My hiring techniques have not changed a lot from 10 years ago. In fact, my feeling is, I always considered it an interview for a data scientist, even though that was not the title.
What sorts of questions are useful to identify the type of candidates the company wanted to hire?
I’m part of the hiring team. Candidates go through five interviews. I’m just one of those! I am an important one, because they will belong to my team.
Because I know that the candidates will be interviewed by other people, I do not view an interview in the classical way.
How do you mean?
Typically an interview with me would last a minimum of four hours, and it could go to eight hours. I am the main speaker. My goal, when I do the interview, is that the candidate is as aware as possible of what will be their daily job, to get the exact feeling of what they will have to do.
I use a lot of examples. And I show them how to dig through data uniques, and it’s a conversation. I ask a lot of technical questions and a lot of functional questions.