J'ai postulé via un recruteur. Le processus a pris 6 semaines. J'ai passé un entretien chez Rakuten (Tokyo) en avr. 2018
Entretien
I went through a first few steps of Rakuten’s interview process which included online coding test, online video interview, making a presentation, and presenting my slides which interviewer could ask question about it and also some extra questions. Besides the fact the process is quite slow, in the last stage that I had to go through I finally got to speak to a person from Rakuten who is working there for about eight years. I didn’t feel like it’s a bad interview but I also felt this is just a formality that everyone needs to go through. The interviewer was basically reading questions from a list. After I finished presenting my self-introduction slides, I had a feeling that no matter how well I answered his questions, I was already judged for some reason and my chances are succeeding are remote. Also, I did not particularly his attitude about a certain question he asked. This is why I also had some doubts in accepting their offer even the outcome was positive. When someone asks what you think our business’s objective, I don’t believe in a million year you should answer “money”. It is obvious everyone wants to make money, who doesn’t!!But I value companies that their main objective is not just money, but rather it is also bringing value to society.
this interview process had 5 rounds
2 was a tech test
3 were conversational in nature
with various managers from direct and indirect to the department manager mostly in Japan but also outside Japan.
1st Interview was simple leetcode task and some questions about Kubernetes. 2nd interview - classical ML and DL questions, Final interview is mostly informal, but there may appear some questions about specific experience and problem solving
Questions d'entretien [1]
Question 1
What is the difference between random forest and boosting?
Initial LeetCode coding test, hiring manager interview for profile and role fit, multiple technical rounds covering statistics, machine learning, SQL, Python, and case studies, concluding with behavioral interviews evaluating teamwork, communication, and cultural alignment.