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      Ricerche correlate: Recensioni su Tinyclues | Offerte di lavoro di Tinyclues | Stipendi di Tinyclues | Benefit di Tinyclues
      Colloqui di TinycluesColloqui per Data Scientist Intern presso TinycluesColloquio di Tinyclues


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      Colloquio per Data Scientist Intern

      22 dic 2021
      Candidato anonimo a colloquio
      Nessuna offerta
      Esperienza neutra
      Colloquio difficile

      Candidatura

      Ho presentato la mia candidatura online. La procedura ha richiesto 3 settimane. Ho sostenuto un colloquio presso Tinyclues

      Colloquio

      Very long interview process for an intern posiiton. Total time spent: +30H -1st step: resume, cover letter, and an application form with a few questions. -2nd step: 1-hour remote technical interview with a Data Scientist (ML questions, coding skills, sorting methods, algorithm complexity calculation). -3rd step: a data science open exercise (feature prediction). They ask you not to spend too much time, but I spent more than 15 hours working on it. -4th and 5th steps: two on-site (Paris) technical interviews. The first one was a regular coding test (1h), and the other one consisted in ML and DL questions, about algorithms and the mathematics behind them (1h15). -6th step: a 1-hour call with the Data Science director and a Data Scientist. I was asked behavioral questions, and then had a ML coding test, completing a Jupyter notebook while sharing my screen. -7th step: I had to optimize my code from step 6, and implement it in the exercise from step 3. It took me quite some time (almost 10h total) Two days after the last step, after 4 interviews, many technical tests and two problems to solve, I received a call to tell me I was not selected for the position.

      Domande di colloquio [1]

      Domanda 1

      How to find the k biggest elements in an unsorted list of n elements? With what complexity? What is the complexity of dichotomy? How to calculate it? And other CS questions. What is a decision tree? How do random forests work? What are the techniques to avoid overfitting? What are the types of regression regularization? What are the classification and regression performance metrics? What are the dimensionality reduction techniques? How does a PCA work (in details)? How do neural networks reduce and increase dimensions? And other ML and DL questions. What are your ambitions as a Data Scientist? What is the project you would like to talk about the most? Why? What techniques did you use? And some behavioral questions.
      Rispondi alla domanda
      1

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