J'ai postulé en ligne. Le processus a pris plus d'une semaine. J'ai passé un entretien chez Booking.com
Entretien
1) Online written test on Machine Learning, statistics.
2) HR interview.
Interview process is sloppy. They will take a written test, screen your CV, ask you for interview and then send you decline e-mail for the next step. The most probable reason is availability of local candidates. Not recommended for candidates living outside Netherlands or EU. It's a waste of time.
Questions d'entretien [1]
Question 1
What is your PhD project? Past job experience etc.
J'ai postulé en ligne. Le processus a pris 3 semaines. J'ai passé un entretien chez Booking.com (Amsterdam) en mars 2019
Entretien
Start with a phone call with HR. They ask generic questions on past experience, had the feeling they tried to match keywords against a checklist. They really want you to name some ML models. The second step was an online multiple choice test on Hackerank. 12 questions, about 80% about statistics, 20% on basic machine learning (cross-validation, feature engineering). The third step was a 45-60 min skype call with 2 Data Scientists. The internet connection was unstable so we backed up on a regular phone call which didn't allow for proper communication. The initial questions are about your experience with ML models: they test your knowledge on as many details as possible. The second part of the interview is solving a business case on the fly. You explain how you'd approach it and why.
Questions d'entretien [3]
Question 1
How does a CNN for object detection compute the detection probabilities?
J'ai postulé en ligne. Le processus a pris 1 semaine. J'ai passé un entretien chez Booking.com en janv. 2017
Entretien
I received a Data Science Challenge within a week of applying. The challenge was an half-an-hour test on HackerRank. The challenge tested conceptual knowledge of machine learning algorithms and statistics. Common topics of testing were clustering, overfitting and hypothesis testing.
Questions d'entretien [1]
Question 1
If the training error and the testing error are both high, as the number of data points increase, what measures will you take to fix the model?