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      Búsquedas relacionadas: Evaluaciones de ID Analytics | Empleos en ID Analytics | Sueldos en ID Analytics | Prestaciones en ID Analytics
      Entrevistas en ID AnalyticsEntrevistas para el cargo de Associate Data Scientist en ID AnalyticsEntrevista en ID Analytics


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      Entrevista para Associate Data Scientist

      21 may 2017
      Empleado anónimo
      Oferta aceptada
      Experiencia positiva
      Entrevista difícil

      Solicitud

      Me postulé en línea. Acudí a una entrevista en ID Analytics en mar 2017

      Entrevista

      The interview process was pretty extensive. There were multiple interview rounds. The first round was a data challenge which tested the candidate's proficiency in using Python or R for data cleaning and preprocessing. The second round was a phone screen (45 - 60 mints) with a Data Scientist. There were questions about my previous data science related work, basic statistics questions, few questions on software engineering fundamentals, machine learning basics. Overall the difficulty level was moderate. The focus was on checking fundamentals. The third round was another phone screen (45 - 60 mints) with a Sr. Data Scientist. This round was more technical and mostly the discussion was around machine learning algorithms and their limitations. Next was onsite at ID's San Diego office. 4 rounds of panel interviews (45 - 60 mints each) with team members and a one-on-one (non-technical) with hiring manager over lunch. All onsite interviews (except with hiring manager) were very technical, focusing on machine learning, programming, statistics, mathematics, map-reduce paradigm, and past work. Overall my experience was very good. All people I met were very respectful, friendly and courteous. All the interviews were more like discussions rather than typical interrogations. Wherever I got struck I was given hints, which I was able to pick and move ahead. Tips: 1. Make sure you know everything mentioned on your resume in good detail. 2. Brush up your machine learning and statistics fundamentals.

      Preguntas de entrevista [1]

      Pregunta 1

      Explain how kNN (k nearest neighbor) algorithm works and compute its complexity.
      Responder pregunta
      3

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