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      Entrevista para Data Analyst 3

      1 de mar de 2021
      Candidato de entrevista anónimo
      Bengaluru
      Oferta rechazada
      Experiencia neutra
      Entrevista difícil

      Solicitud

      Me postulé en línea. El proceso tomó 3 semanas. Acudí a una entrevista en Zynga (Bengaluru) en feb 2021

      Entrevista

      The overall process for a Data Analyst profile would be around 5-6 rounds each of >=1+ hours as follows: 1. Screening technical round by Analytics Lead : Covers 3 aspects - SQL, programming language of your choice (R/Python) and business case study 2. Core technical by Senior Data Analyst/Data Scientist - SQL + R/Python 3. Product round - Tests business and problem-solving skills 4. Director round - General discussion and business understanding 5. Culture Fitment - Analytics Lead/Director 6. HR (optional) - for questions related to compensation, behavioral per se. Note that the first 2 rounds happened on Coderpad. For second round, I was given an option for Zoom call.

      Preguntas de entrevista [1]

      Pregunta 1

      Round1: Technical 1. Walkthrough of CV and any one project explanation in depth (from identifying business problem, solution approach, caveats and impact). This was followed by interviewer's questions on why a particular approach, what-if we used so and so approach etc. 2. SQL problems (easy to medium difficulty): a. Given Employee and Department table schemas, display the employees who have the highest salary in the department. b. Using the same tables, display number of employees in each department. 3. Python (2 questions) : a. Create a dataframe from a given list of example fields and column names b. Given a string, write a function to check if the string is a palindrome or not. 4. Business case study: The interviewer asked me if I have played any mobile games. Since I mentioned Scrabble Go, the case question was based on Scrabble: a. How would you design an algorithm to match one player with another for an online game setup? What data points/metrics will you use and why? How would you define the success metrics for the match-making algorithm? You can assume that only players can play only in online mode. You have the one-month historic data for the player stats on Scrabble and for the sake of simplicity, assume there are no user types (guest/logged in etc.) b. This was followed by in-depth discussion of the approach I shared and what-if scenarios based questions? Round 2: Technical 1. Walkthrough of CV and 2 projects in depth (~30 mins) 2. SQL questions: a. Given Daily Active Users table with the fields (Player ID, DateTimestamp, GameID), calculate the moving weekly count of daily active users i.e. on a given day, calculate the count of players who have played either today or in the last 6 days) for the month of January 2021. b. Given another table of Level Ups with the fields (Player ID, Player level, DateTimestamp when the player reached the certain level). Note that this table would contain records only for the day a player advanced a level in the game. For all the active users, calculate the daily distribution of players (in percentage) across 3 bins of levels (say 0-100, 100-200, >200). 3. Python (how would you rate yourself on a scale of 1-10?) a. Given a list of elements, write a function to return the index of an element if it is present in the list. b. Given 2 lists of string elements, write a function to return cross-join and inner join of both lists.
      1 respuesta
      10

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