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Data Scientist Interview Questions

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Data Scientist Interview Questions

Lors d’un entretien pour le poste de data scientist, les employeurs vont poser des questions leur permettant d’évaluer vos compétences en modélisation des données, résolution des problèmes et programmation. Soyez préparé à répondre à des questions générales testant vos connaissances en statistiques et en science des données. Vous devez également être prêt à répondre à des questions ouvertes permettant de tester votre créativité, vos compétences en communication et votre éducation formelle en modélisation des données et en programmation.

Questions d'entretien d'embauche fréquentes pour un data scientist (H/F) et comment y répondre

Question 1

Question 1 : Quelles techniques de modélisation des données préférez-vous et pourquoi ?

How to answer
Comment répondre : La transformation des données en informations compréhensibles et exploitables est un élément critique du métier de data scientist. Cette question permet aux employeurs de comprendre vos compétences en modélisation des données et votre cursus. Répertoriez et détaillez les techniques de modélisation des données que vous préférez, notamment leurs avantages comme leur facilité d’utilisation, leur flexibilité, etc.
Question 2

Question 2 : Comment détectez-vous les faux comptes Instagram utilisés pour escroquer les clients ?

How to answer
Comment répondre : Ce type de question permet à un employeur de tester vos compétences en résolution des problèmes. Lorsque vous répondez à des questions ouvertes comme celle-ci, n’hésitez pas à demander des précisions sur ces dernières et à utiliser un tableau pour présenter vos compétences en programmation et création de graphiques. Partagez votre processus de réflexion lorsque vous résolvez le problème.
Question 3

Question 3 : Décrivez des situations qui requièrent une liste, un uplet ou un ensemble sur Python.

How to answer
Comment répondre : Les intervieweurs posent ce type de question pour tester vos compétences en programmation sur Python. Révisez les rudiments de Python comme les listes, les uplets et les ensembles avant votre entretien. Vous devez être en mesure d’expliquer à quel moment et de quelle manière chaque outil est utilisé par les data scientists.

54,351 data scientist interview questions shared by candidates

1. Started with a detailed explanation of a past project - what was the business question, how did you come up with the solution, what was your hypothesis, how did you design the A/B test, why did you make certain choices, what was the result etc. Prepare 1-2 examples from your past, where you can talk in depth about the technical elements of your project. 2. Let's say we have a dataset with attributes for a house (Sq footage, locality etc) and house price. How will you predict the house price from these attributes? (Build a multiple regression model) 3. For this multiple regression model, explain the end-to-end process. What steps will you take before building the model, how will you impute missing values, how will you handle outliers etc. What are the underlying assumptions of a regression model? 4. Once the model is built, how will you infer the relationship (sign and magnitude) between the house attributes and house price. How will you explain it to someone that's not a technical person? 5. For the regression coefficients, how will you interpret them, (p-values, confidence interval etc). How will you explain a p-value to a layman 6. Next question was about "how will you segment customers" in order to serve a business requirement, such as determining which customers to show a given ad (I answered with clustering, because the business problem wasn't very specific, he just described it very generally) 7. For clustering, how does it work, how to choose the value of K in k-means. I also said we can use Gaussian mixture models for clustering, which he didn't seem to know because he asked me to clarify what I mentioned. There might have been a few more questions that I don't remember, but the theme of the interview was to check how well you know the basics of Stats/ML. I believe I answered most of the questions correctly so to receive the feedback that I wasn't up to the mark technically seemed like a case of Google not wanting to reveal the real reason, whatever it was. Either way, make sure you confirm the format of the interview with the recruiter. Because I was already interviewing with other companies, I had brushed up on my Stats/ML basics, but you might not be as lucky. Good luck!
avatar

Marketing Data Scientist

Interviewed at Google

4.4★
Nov 19, 2020

1. Started with a detailed explanation of a past project - what was the business question, how did you come up with the solution, what was your hypothesis, how did you design the A/B test, why did you make certain choices, what was the result etc. Prepare 1-2 examples from your past, where you can talk in depth about the technical elements of your project. 2. Let's say we have a dataset with attributes for a house (Sq footage, locality etc) and house price. How will you predict the house price from these attributes? (Build a multiple regression model) 3. For this multiple regression model, explain the end-to-end process. What steps will you take before building the model, how will you impute missing values, how will you handle outliers etc. What are the underlying assumptions of a regression model? 4. Once the model is built, how will you infer the relationship (sign and magnitude) between the house attributes and house price. How will you explain it to someone that's not a technical person? 5. For the regression coefficients, how will you interpret them, (p-values, confidence interval etc). How will you explain a p-value to a layman 6. Next question was about "how will you segment customers" in order to serve a business requirement, such as determining which customers to show a given ad (I answered with clustering, because the business problem wasn't very specific, he just described it very generally) 7. For clustering, how does it work, how to choose the value of K in k-means. I also said we can use Gaussian mixture models for clustering, which he didn't seem to know because he asked me to clarify what I mentioned. There might have been a few more questions that I don't remember, but the theme of the interview was to check how well you know the basics of Stats/ML. I believe I answered most of the questions correctly so to receive the feedback that I wasn't up to the mark technically seemed like a case of Google not wanting to reveal the real reason, whatever it was. Either way, make sure you confirm the format of the interview with the recruiter. Because I was already interviewing with other companies, I had brushed up on my Stats/ML basics, but you might not be as lucky. Good luck!

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