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      Colloqui di Domino Data LabColloqui per Customer Success Engineer presso Domino Data LabColloquio di Domino Data Lab


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      Colloquio per Customer Success Engineer

      14 ago 2017
      Dipendente anonimo
      San Francisco, CA

      Altre recensioni di colloqui per Customer Success Engineer presso Domino Data Lab

      Colloquio per Customer Success Engineer

      21 feb 2017
      Candidato anonimo a colloquio
      Nessuna offerta
      Esperienza negativa
      Colloquio facile
      Offerta accettata
      Esperienza positiva
      Colloquio difficile

      Candidatura

      Ho presentato la mia candidatura tramite segnalazione di un dipendente. La procedura ha richiesto 5 settimane. Ho sostenuto un colloquio presso Domino Data Lab (San Francisco, CA) nel mese di lug 2017

      Colloquio

      Having been both an individual contributor as well as a hiring manager with multiple companies, it is not an exaggeration when I say I've reviewed ~1000 resumes and been involved in ~100 interviews. The operative word is resume. Due to rigid HR screening and traditional recruiters, the recruiting process hinged on resume review rather than actionable qualifiers. Domino was a refreshing process, beginning to end. Rather than asking me to regurgitate my work experience, we discussed culture, what motivates me, engaged in role playing, and partook in a rather rousing take home challenge. The challenge allowed me to get creative on how to take an NLTK classification model from ideation to model deployment via the platforms powerful, yet simple, API endpoints. Imagine that, hiring based on merit over resume writing

      Domande di colloquio [3]

      Domanda 1

      Explain containers to your grandmother
      Rispondi alla domanda

      Domanda 2

      What is one time in your career you are especially proud of?
      Rispondi alla domanda

      Domanda 3

      Series of questions regarding the solution I created for the take home challenge
      Rispondi alla domanda
      7

      Candidatura

      Ho presentato la mia candidatura online. La procedura ha richiesto 2 settimane. Ho sostenuto un colloquio presso Domino Data Lab nel mese di gen 2017

      Colloquio

      1 Phone screen and 1 take-home challenge. Phone screen was simple and straightforward but the interviewer didn't get impressed and asked very generic questions. Nowadays companies like this will always ask you why you want to do Data Science. Feel like the interviewer only want to finish routine questions and pass you the next step - a take home challenge. Take home challenge was about how QUICKLY you can get familiarized with Domino's data platform. To be honest, it's a fair evaluation as this position requires troubleshooting, constantly. I can see the goal is to find people who can learn new technologies and find a solution quickly. I have never used Domino as a data analytics platform, so I started from scratch. Spent my whole night (after work) to finish the challenge which included reading their FAQ and tutorial videos etc. Wrote a simple document on what/how I solved the problems, step by step. I admit that it took longer than I expected to solve the problem, but I tried my best. I am a 5-year experienced R coder so the coding part was like a breeze. The hard part is to navigate/use Domino's platform. It was such a poor design! Too much to talk about. Perhaps because they are still a young start up. Long way to go to become a real product. I hold my respect to them but the thing annoys me was that they won't provide feedback after my followup email. My conclusion is that, they could be 'using' candidates to do product testing, and gather user experience for free. I know some start-up do that. It's just bad if that's the case. I wish you good luck but if you want to replace RStudio/Jupyter, you have a long way to go.

      Domande di colloquio [3]

      Domanda 1

      Why Domino?
      Rispondi alla domanda

      Domanda 2

      Why data science?
      Rispondi alla domanda

      Domanda 3

      Describe your current job
      Rispondi alla domanda
      6