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      Entretien pour Data Scientist

      12 avr. 2024
      Candidat à l'entretien anonyme
      Aucune offre
      Expérience négative

      Autres retours d’entretien d’embauche pour un poste comme Data Scientist chez Rakuten

      Entretien pour Data Scientist

      1 avr. 2026
      Employé (anonyme)
      Offre acceptée
      Expérience négative
      Entretien facile

      Candidature

      Entretien moyen

      Candidature

      J'ai postulé via un recruteur. J'ai passé un entretien chez Rakuten en avr. 2024

      Entretien

      The interview process was very long, spanning 3 months and with a lot of steps. I had a first interview with the general manager, that was supposed to be a simple initial discussion but quickly got a bit technical. After passing this one, I had two technical interviews with different persons on the team I should have joined. They asked me about my past projects in detail and I got an example case study to explore in discussion. While waiting for an answer, I also had to pass an online English test, oral and written, as well as completing a quite invasive background check where I had to provide the contact information of one colleague and one manager for them to answer questions about me as a person. I passed all of those interviews and other steps successfully and I had a discussion with HR about the salary and the starting date. I thought it was finally over, after 2 months of interviews ! I was told the team was eager to hire me, and I just needed to wait to receive the contract. But after two weeks of waiting, in a very inappropriate move, I was asked to do another technical interview, with some other general manager. It ended up being a live coding test, in less than 30 minutes, on a problem I had never worked on and without being able to use any resources. I was not told to prepare for that, and I did pretty poorly. Finally, after almost two weeks of more waiting, I was told that this person's feedback resulted in my candidacy being rejected. I cannot recommend anyone to work with a company whose hiring process is this chaotic. There is zero consideration for the candidate time and efforts, which are probably a good sign of management deficiencies at Rakuten.

      Questions d'entretien [2]

      Question 1

      Describe your past projects and explain your choice of model architecture and which technologies you used.
      Répondre à cette question

      Question 2

      Discussion about an example use case on LLMs where using LangChain would be appropriate.
      Répondre à cette question
      3

      J'ai passé un entretien chez Rakuten

      Entretien

      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.
      1

      Entretien pour Data Scientist

      1 oct. 2025
      Candidat à l'entretien anonyme
      Tokyo
      Aucune offre
      Expérience neutre
      Entretien facile

      Candidature

      J'ai passé un entretien chez Rakuten (Tokyo)

      Entretien

      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?
      Répondre à cette question

      Entretien pour Data Scientist

      3 sept. 2025
      Candidat à l'entretien anonyme
      Tokyo
      Aucune offre
      Expérience neutre
      Entretien moyen

      Candidature

      J'ai passé un entretien chez Rakuten (Tokyo)

      Entretien

      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.

      Questions d'entretien [1]

      Question 1

      Statistics (p-value, hypothesis testing), ML (overfitting, bias-variance, metrics), SQL (joins, aggregations, ranking), Python (data cleaning, algorithms), Case studies (churn prediction, A/B testing, recommendation systems).
      Répondre à cette question