Top Data Scientist Interview Questions with Sample Answers

By Indeed Editorial Team

Updated December 4, 2022

Published October 18, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you're seeking a job in the field of data science you might find yourself wondering about common data science interview questions, and how you should prepare for them. These questions typically require you to demonstrate your technical knowledge and industry skills. By understanding how to answer these questions, you can increase your chances of being hired. In this article, we discuss several common interview questions for data scientists, ranging from general questions to those seeking in-depth information about your experience and background. We also answer frequently asked questions for data scientists and provide several example interview questions and answers to help you prepared for your own interview.

Related:

  • How to Become a Data Scientist in 5 Essential Steps

  • Everything You Need to Know About Data Science as a Career

General data science interview questions

General data science interview questions typically require you to answer how you can contribute to the company. These questions may ask about your general knowledge and how you work. Here is a list of general interview questions for data scientists:

  • What are the differences between supervised and unsupervised learning?

  • What is your preferred programming language?

  • How would you streamline our processes?

  • What is general bias?

  • Why would you conduct resampling?

  • How often would you update our algorithms?

  • What is root cause analysis?

  • What is the law of large numbers?

  • After reviewing our website and social media platforms, which forms of data would you like to review?

  • How would this job help you further your career goals?

Data science interview questions about experience and background

Questions about your experience and background provide employers with a clear indication of what skills you have, and how you can contribute to their team. Here is a list of interview questions related to the experience and background of data scientists:

  • Have you ever built a random forest model?

  • Based on your experience, how would you define the difference between univariate, bivariate, and multivariate analysis?

  • When selecting variables, which feature selection methods do you use?

  • When have you used confounding variables?

  • Have you used collaborative filtering?

  • How would you describe recommender systems?

  • Where did you obtain your postsecondary degree, and how did you learn about data science?

  • Have you ever declared a time series as stationary?

  • When have you used p-values to prove your hypothesis?

  • Have you ever used k-means?

Related: Data Science Specialization: Types, Benefits, and Tips

In-depth data science interview questions

During an interview, you are also likely to be asked a series of in-depth questions that require more complex explanations. Here is a list of some common in-depth data science interview questions:

  • What is logistic regression, and how do you perform it?

  • What are the steps required to make a decision tree?

  • How do you avoid overfitting your model?

  • What are eigenvalue and eigenvector, and how would you use them?

  • Have you used A/B testing? If so, what was your goal?

  • When do you use feature vectors, and what are they?

  • Let's say your organization develops a website that allows visitors to obtain randomly generated coupons, and they may also obtain no coupons. Which analysis method would you use to determine the impact of each coupon, or lack thereof, on company sales?

  • Let's say you study the behaviour of particular groups of people, and later identify that there are four types of consumers affecting your study. Which algorithm would you use to determine every user that relates to these individual types?

  • What was your most rewarding day as a data scientist?

  • How do you treat outliers?

Frequently asked questions about data science interviews

Here are answers to several frequently asked questions about interviews for data scientists and what you can expect:

How do I prepare for a data science interview?

When preparing for your interview, you may want to review information about data cleaning, statistics basics, JavaScript, and various computer programming languages. This helps you to brush up on the technical skills required to answer questions in your interview. You may also want to research popular analytics tools to develop an understanding of general data and its impact on companies.

Related: 17 Interview Tips to Help You Get the Job

Do hiring managers ask coding questions?

Hiring managers may not ask you specific coding questions, but you can expect general questions about this part of the job. For example, some hiring managers ask questions about data structure. General coding questions determine whether you have the technical skills required for the position.

Related: What Are Data Scientist Skills? (With Definition and Roles)

How long are data science interviews?

The length of your interview will likely depend on your experience and the exact position of interest. Data science interviews typically require candidates to meet several interviewers and people, which increases interview time. You may also require several interviews. When attending several interview rounds, you can expect a total interview time of up to eight hours.

Related: Step-By-Step Guide on How to Stand Out in an Interview

Sample data science interview questions with answers

Reviewing example questions and answers is a helpful way to get clarity on what you might be asked so you can prepare answers that you're comfortable with. Here are several commonly asked questions with sample answers:

How can you use a confusion matrix to calculate accuracy?

Hiring managers may ask this question to identify whether you have the required technical skills to complete the job. Here is an example of how you may answer this question:

Example: "A confusion matrix provides the values for total data, actual values, and predicted values. The formula for these equations requires you to add the true positive and the true negative. From here, you divide those sums by the total observations."

What do you know about random forests?

This question identifies whether you have the skills required to conceptualize theoretical knowledge and implement it in practice. Here is an example of how you can explain random forests:

Example: "Random forests refer to learning algorithms that combine various learning models to improve results. They build several decision trees and combine them for more accurate predictions. To work toward a random forest, you typically build many decision trees depending on data samples. From here, you consider splits and choose sample predictors on each tree. You make decisions depending on the majority rule."

Related: 16 of the Best Courses for Data Science (With Skills)

What is cross-validation?

This question assesses your knowledge of various data science techniques. Hiring managers ask this question to filter candidates based on their level of knowledge. Here is an example answer for you to consider:

Example: "Cross-validation refers to a validation technique that determines the generalizations of independent data sets depending on the outcome of statistical analysis. Professionals typically use cross-validation in backgrounds, where they want to determine the accuracy of models and when they develop forecasts. Cross-validation provides professionals with insight into the generalization of data sets."

What are RMSE and MSE, and how do you use these terms in linear regression models?

These questions determine your background experience and provide the hiring manager with information about your general work process. Here is an example answer explaining RMSE and MSE:

Example: "MSE refers to the mean squared error, while the RMSE refers to the root mean squared error. MSE refers to the difference between predicted values and original values. RMSE refers to the error rate depending on the square foot of MSE. Linear regression models use these values to determine measures of accuracy."

What are recommender systems?

Technical questions about recommender systems aim to determine whether you can help websites and companies increase their sales and engagement. Here is an example answer explaining recommender systems:

Example: "Recommender systems suggest items to users and customers. These are useful for those who want to sell or recommend items related to the users' views. For example, streaming websites for various television shows and movies are recommender systems. When you watch television shows with those systems, streaming services recommend other television shows. You may divide these systems into collaborative filtering and content-based filtering.

Collaborative filtering refers to recommendations that depend on a user's interests. If you work for a company that sells products and services, the website may recommend additional items depending on those in the user's cart. Content-based filtering refers to recommendations depending on similar properties. For example, those looking for songs similar to ones they already appreciate may look for content-based filtering."

What are the steps involved in maintaining deployed models?

This question identifies your previous experience and whether you have worked with deployed models in the past. Here is a sample answer for you to consider:

Example: "The steps involved in maintaining deployed models are monitor, evaluate, compare, and rebuild. Monitoring allows you to determine the accuracy and the impact of processes. The evaluation step determines whether your current model requires a new algorithm. You compare your models to determine which one has the best performance. Finally, the rebuilding phase requires you to build your best performing model for current data sets."

Please note that none of the companies, institutions, or organizations mentioned in this article are affiliated with Indeed.

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