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      Kaizen Gaming

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      Data Science Team Lead Interview

      7 Feb 2025
      Anonymous interview candidate
      No offer
      Positive experience

      Other Data Science Team Lead interview reviews for Kaizen Gaming

      Data Science Team Lead Interview

      18 May 2023
      Anonymous employee
      Accepted offer
      Positive experience
      Average interview

      Application

      I applied online. The process took 2 weeks. I interviewed at Kaizen Gaming in May 2023

      Average interview

      Application

      I applied online. The process took 2 months. I interviewed at Kaizen Gaming in Jan 2025

      Interview

      I was first contacted by a recruiter and setup a one hour interview. The recruiter knew a lot about the position and was very helpful. We discussed my background, company fit, values, starting date and salary expectations. Unfortunately the company offers rather low salaries for the skillset they are asking for. After that I had a technical interview. The interview was very well designed and went through a case study about a grad detection system. We explored the problem, talked about stakeholder management but also dove very deep technically.

      Interview questions [1]

      Question 1

      Several questions about Data science
      Answer question

      Interview

      There was an initial screening by the HR team and then one 2-hour technical interview regarding everything around applied data science. After that, there was an additional 1-hour technical interview around MLOps and engineering (incl. live coding and code review) followed by a final interview with HR mostly around Kaizen culture and some team leading-related questions. The overall process was very quick, all people were friendly and there was no take-home exercise which meant there was less time needed to devote in the interview process.

      Interview questions [1]

      Question 1

      Anything on applied data science incl. feature engineering, feature selection, modeling, model tuning, model evaluation.
      Answer question
      1