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      Entretien pour Data Scientist, Analytics

      6 mars 2019
      Candidat à l'entretien anonyme
      Menlo Park, CA
      Aucune offre
      Expérience négative
      Entretien moyen

      Candidature

      J'ai postulé via la recommandation d'un employé. Le processus a pris 2 mois. J'ai passé un entretien chez Meta (Menlo Park, CA) en févr. 2019

      Entretien

      Standard process. I applied through an employee referral. Recruiter reached out and talked to me for about 30 minutes. He was really nice and through the entire process I felt like he really wanted me to succeed. First step was 45 minute Bluejeans interview with a data science manager (2 SQL questions + 2 product sense questions). Second step was onsite at Menlo Park with 4 rounds of interviews (SQL, probability and stats and product sense). Here are some of my observations about this role/ company: 1) Everyone who interviewed me was in their 20's. If you're older, it will work against you (you may not be "fit" for their culture). 2) If you're female and/or Asian, you have a leg up. I noticed more than 50% of people on campus were Asian. FB loves Asians. 3) This role is not really a data science role. It's more like a dumb version of product data analyst. You will work with a product owner and will run queries to answer questions. Don't expect to do sophisticated modeling or machine learning. 4) SQL, stats and probability questions are fair game and if you practice and study you can answer them. Product sense questions however are crap shoot. I personally thought my answers were good but for some reason the interviewer didn't like my answers.

      Questions d'entretien [3]

      Question 1

      We have a table called ad_accounts(account_id, date, status). Status can be active/closed/fraud. A) what percent of active accounts are fraud? B) How many accounts became fraud today for the first time? C) What would be the financial impact of letting fraud accounts become active (how would you approach this question)?
      6 réponse(s)

      Question 2

      You have 24 hours, how would you measure what % of FB stories are fake news? How would you refine your process if you have more time?
      2 réponse(s)

      Question 3

      We have two types of reviewers: careful reviewer (80% of reviewers) and lazy reviewers (20% of reviewers). Careful reviewers rate a post positive 60% of time and negative 40% of time). Lazy reviewers however rate a post positive 100% of time. A) what is the probability that a random ad is reviewed positively? B) If an ad gets a negative review, what is the probability that it's reviewed by a lazy reviewer? C) If 3 ads are reviewed positively in a row, what is the probability that they are reviewed by a lazy reviewer? D) Some as above with n positively reviewed ads in a row. What happens when n goes to infinity? E) If we have very few labeled data, how can we build a model to distinguish between careful and lazy reviewers?
      2 réponse(s)
      36

      Autres retours d’entretien d’embauche pour un poste comme Data Scientist, Analytics chez Meta

      Entretien pour Data Scientist - Analytics

      8 févr. 2024
      Candidat à l'entretien anonyme
      Aucune offre
      Expérience positive
      Entretien moyen

      Candidature

      J'ai postulé via la recommandation d'un employé. Le processus a pris 4 semaines. J'ai passé un entretien chez Meta en févr. 2024

      Entretien

      Firstly, the recruiter emailed me some questions, including basic inquiries about my background, projects I've worked on, and how I approach product cases. The interview process consisted of two rounds, each comprising a mix of technical questions, behavioral assessments, and product case studies. In the initial round, the focus was on technical proficiency, evaluating my knowledge and skills relevant to the role. This involved discussing past projects in detail, demonstrating problem-solving abilities, and assessing my understanding of key concepts within the field. The second round delved deeper into behavioral questions, aiming to gauge my interpersonal skills, communication abilities, and how I approach challenges in a team environment. Additionally, I was presented with more complex product cases, requiring me to analyze scenarios, propose solutions, and articulate my reasoning effectively. Overall, the interview process provided a comprehensive assessment of both my technical capabilities and my suitability for the role in terms of teamwork, problem-solving, and product understanding.

      Questions d'entretien [1]

      Question 1

      Some python and sql questions. Some product cases, for example, they launched reel, but the time spent decreased. they asked you to provide reason and solution.
      Répondre à cette question
      1

      Entretien pour Data Scientist, Analytics

      15 janv. 2022
      Candidat à l'entretien anonyme
      Aucune offre
      Expérience neutre
      Entretien moyen

      Candidature

      J'ai postulé en ligne. J'ai passé un entretien chez Meta

      Entretien

      Recruiter call followed by an hour-long interview. I didn't make it to the next round, but I know that it consists of 4 different interviews. There are several resources online to prepare

      Questions d'entretien [1]

      Question 1

      SQL question, related to joins, timestamps
      Répondre à cette question

      Entretien pour Data Science, Analytics

      5 nov. 2021
      Candidat à l'entretien anonyme
      Aucune offre
      Expérience positive
      Entretien difficile

      Candidature

      J'ai postulé en ligne. J'ai passé un entretien chez Meta

      Entretien

      The first section of the interview was a case analysis, and the second section of the interview covered two SQL questions. The SQL questions were quite similar to Leetcode's "medium" difficulty questions – I would recommend using Leetcode to prepare for your Facebook DS/Analytics interviews!

      Questions d'entretien [1]

      Question 1

      Two SQL questions and one case question.
      Répondre à cette question

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