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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.

54,353 data scientist interview questions shared by candidates

3 main questions: 1. write an algorithm to generate the probability of obtaining a random number based on a prior discrete distribution. Was asked all the relevant probability theory questions where applicable. asked to solve with and without using numpy library functions. 2. pick any ML algo of my choice and explain it at depth - decision trees in my case - how I have deployed it in a project before - covered all the conceptual details. 3. write SQL queries to offer discounts to customers that may not retain a premium subscription service - how would you determine who will/will not be a recurring customer?
avatar

Data Scientist

Interviewed at Intuit

4.1★
Nov 12, 2023

3 main questions: 1. write an algorithm to generate the probability of obtaining a random number based on a prior discrete distribution. Was asked all the relevant probability theory questions where applicable. asked to solve with and without using numpy library functions. 2. pick any ML algo of my choice and explain it at depth - decision trees in my case - how I have deployed it in a project before - covered all the conceptual details. 3. write SQL queries to offer discounts to customers that may not retain a premium subscription service - how would you determine who will/will not be a recurring customer?

Some questions can also be presented in the form of a simple case exercise such as how would you process a dataset for training and how would you decide what model to use. Interviewers at all rounds will always ask follow-up questions to your answers so if you suggest an idea or approach, be sure you are familiar with it. They may also ask you about keywords you wrote on your resume. For example, if you wrote “GANs” somewhere on your resume, don’t be surprised if they ask you to explain GANs to them in detail with a bunch of follow-up questions. Make sure you brush up on fundamentals of ML and statistics Science Case study Describe how you will generate your dataset: how you will select an unbiased sample, deal with class imbalance, consider temporal effects. How will you split into train/val/test? I follow this website: https://mlengineer.io/
avatar

Data Scientist

Interviewed at Amazon

3.5★
Oct 6, 2021

Some questions can also be presented in the form of a simple case exercise such as how would you process a dataset for training and how would you decide what model to use. Interviewers at all rounds will always ask follow-up questions to your answers so if you suggest an idea or approach, be sure you are familiar with it. They may also ask you about keywords you wrote on your resume. For example, if you wrote “GANs” somewhere on your resume, don’t be surprised if they ask you to explain GANs to them in detail with a bunch of follow-up questions. Make sure you brush up on fundamentals of ML and statistics Science Case study Describe how you will generate your dataset: how you will select an unbiased sample, deal with class imbalance, consider temporal effects. How will you split into train/val/test? I follow this website: https://mlengineer.io/

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