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Data Scientist Interviews

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Data Scientist Interview Questions

In a data scientist interview, expect employers to ask questions that assess your data modeling, problem-solving, and programming skills. Be prepared to answer general questions that test your knowledge of statistics and data science. You should also be ready to answer open-ended questions that test your creativity, communication skills, and formal education in data modeling and programming.

Top Data Scientist Interview Questions & How to Answer

Question 1

Question #1: Which data modeling techniques do you prefer and why?

How to answer
How to answer: Turning data into understandable and actionable information is a critical part of the data scientist's job. This question allows employers to understand your data modeling skills and background. List and discuss your preferred data modeling techniques, including benefits such as ease of use, flexibility, etc.
Question 2

Question #2: How would you detect bogus Instagram accounts used for scamming consumers?

How to answer
How to answer: Questions like this one allow an employer to test your problem-solving skills. When answering open-ended questions such as these, feel free to ask clarifying questions and use whiteboards to demonstrate your coding and diagramming skills. Share your thought process as you work through the problem.
Question 3

Question #3: Describe circumstances that require a list, tuple, or set in Python.

How to answer
How to answer: Interviewers will use questions such as this one to test your Python programming skills. Review Python basics such as lists, tuples, and sets before your interview. You should be able to explain when and how each tool is used by data scientists.

54,376 data scientist interview questions shared by candidates

Asking about topics from my cv: supervised&unsupervised learning mostly. 1)What is linear regression ? error and reguarization methods (L1&L2) 2)How to determine k in k-means? 3)difference between GMM and k-means. 4)Explain PCA. 5)How to decide how many PC (in PCA) to use? 5)What is t-SNE? 6)Explain the difference between bootstrap and bagging and give example for one model for each one of them.
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Data Scientist

Interviewed at Data Science Group

3.8★
Dec 21, 2019

Asking about topics from my cv: supervised&unsupervised learning mostly. 1)What is linear regression ? error and reguarization methods (L1&L2) 2)How to determine k in k-means? 3)difference between GMM and k-means. 4)Explain PCA. 5)How to decide how many PC (in PCA) to use? 5)What is t-SNE? 6)Explain the difference between bootstrap and bagging and give example for one model for each one of them.

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