20 Data Scientist Interview Questions + Tips (2024 Guide)

When you’ve landed an interview for a data scientist position, one of the best ways to improve your chances of success is by preparing in advance with practice interview questions and answers. By thoroughly engaging in data science interview prep, you'll be ready to showcase your knowledge and expertise to the hiring manager when the time arrives.

To build confidence for your next interview, explore some of the most common interview questions for data scientists and tips for answering them below. Later, you'll also discover some cost-effective online courses that can help you ace your next interview.

Top data scientist interview questions to know in 2024

Below, you’ll find a list of some of the most common types of data scientist interview questions, covering everything from coding and data modeling to algorithms and statistics. Preparation is key to ensuring you confidently enter your next data science interview.

Data science coding and programming questions

• What would you do if a categorization, an aggregation, and a ratio came up in the same query?

• Calculate the Jaccard similarity score between two sets: the size of the intersection divided by the size of the union.

• Write a program that prints numbers from one to 50 in a language of your choice.

• List all orders, including customer information, using a basic SQL query.

Interviewers like to ask about your prior experience with such common programming languages as Python, R, and SQL because coding is an essential skill for data science roles, regardless of the company in which you’re working.

Typically, these data scientist technical interview questions will involve data manipulation using code devised to test your programming, problem-solving, and innovation skills. During the interview, you’ll likely need to use a computer or whiteboard to complete the questions, or your interviewer may ask you to verbally explain your thought process.

Read more: Most Popular Programming Languages

Data science questions on algorithms

While the exact questions will vary from one interview to another, you can review some of the most common forms they may take in the following examples:

• How would you reverse a linked list?

• The recommendation “People who bought this also bought…” seen on many e-commerce sites results from which algorithm?

• If we are looking to predict the probability of death from heart disease based on three risk factors (age, sex, and high levels of cholesterol), what is the most appropriate algorithm to use?

• How often should an algorithm be updated?

Questions on algorithms are primarily designed to test how you think about a problem and demonstrate your knowledge. Understanding algorithms is important because they undergird much of the work you’ll be doing as a data scientist.

During your interview, you’ll likely need to explain the purposes of various algorithms as well as how they might help solve different problems and demonstrate your knowledge of machine learning algorithms. With this in mind, make sure you are familiar with common algorithms such as linear regression and logistic regression.

Data science questions on data modeling techniques

• How should you maintain a deployed model?

• Can you name a disadvantage of using the linear model?

• What is regularization in regression?

• What is a confusion matrix?

Your interviewer will most likely ask questions on data modeling techniques to see how familiar you are with different data models and their uses. Interviewers ask questions of this type to test your knowledge of building statistical models and implementing machine learning models, such as linear regression models, logistic regression models, and decision tree models.

Build your data science portfolio

Explore hands-on data science projects with these beginner-friendly guided projects on Coursera.

Read more: 7 Machine Learning Projects to Build Your Skills

Data science statistics and probability questions

• What is the law of large numbers?

• What is selection bias?

• What is the process of working toward a random forest?

• Give an example of a data type with a non-Gaussian distribution. 

Interviewers ask questions on statistics in a data scientist interview to test your knowledge of statistical theory and principles. They do this because statistics are a cornerstone concept in data science.

This is your chance to display your proficiency in common statistical analysis methods and concepts, so make sure to refresh your knowledge before the big day. Some common topics to review include random sampling, systematic sampling, and probability distribution.

Data science questions on product sense and business applications

• We are looking to improve a new feature for our product. What metrics would you track to make sure it’s a good idea?

• If the company wanted to grow X metric on X feature, how might we achieve that?

• Tell me about a time you set about aligning data projects with company goals.

• When measuring the impact of a search toolbar change, which metric would you use?

These questions will often be particular to the role, but you can use the questions above as guides. Most likely, you can expect the interviewer to ask how your work might contribute to the growth of the business and the development of the goods or services it sells. Many employers are more interested in the impact that effective data scientists will have on their bottom line than they are in exploring the field academically.

These questions are specific to the business and how you would use data science in your job. By answering them effectively, you can demonstrate your ability to apply your data science knowledge in a business capacity rather than simply showing your grasp of theory.

Tips for data science interview prep

Thoroughly practicing for your interview is perhaps the best way to ensure its success. To help you get ready for the big day, review the tips below to prepare for whatever comes up.

1. Research the position and the company.

If you want to know what an employer may ask you during your data science interview, the best place to start is by researching the role to which you are applying, as well as the company itself.

Check out company websites, social media pages, and reviews, and consider even trying to speak to people who already work there. The more you can glean about the work culture, the company’s values, and the methods and systems they use, the better you can tailor your answers and demonstrate you're fully aligned with the organization's goals.

By researching your role, you can also better predict some of the questions you may encounter. Go through the job description and see what the employer expects, as this will likely be what they evaluate you on. Make sure you have an example prepared for each point and prepare a good bank of potential answers to any question. 

2. Review the job description, the role, and its responsibilities.

As you review the job description and responsibilities for the position, try to get a clear sense of what the employer will expect of you. If you spot anything in the job description that you don’t understand, search the internet, look up the terms, or call the company and ask for clarification.

If you fully understand expectations, then it will be easier to tailor your answers and give highly relevant examples. By demonstrating the value you will add to the business with clear responses and concrete examples, you’ll highlight not only your qualifications for the position but also the real-world impact of your work.

3. Practice answering common interview questions for data scientists.

After finishing your research and reading this article, you should have some idea of what to expect in the interview. Write these questions down and rehearse your answers.

It might feel strange, but the best way to do this is to speak out loud as if you are talking to the interviewer in person. Doing it aloud means you can really hear how your answers will sound, which can help you practice your volume, speed, and body language. The more you practice, the easier the answers will come to you, and the more prepared you will be to recall the information during the interview itself.

Read more: Practice Interview Questions: How to Tell Your Story

Tip: Have questions ready

While it’s important to be thinking about the questions you’ll have to answer, it’s also essential to have some questions ready that you will ask at the end of the interview. Some examples of questions include:

• What is the metric on which you’ll evaluate my performance?

• How will the projects I work on align with key business goals?

• What are the top three reasons you like working here?

• What are the projects that need to be addressed most immediately?

It’s easy to overlook this, but it is an excellent way for you to find out more about the role and decide whether it is definitely for you while also showing your interest in the position and company.

Read more: Questions to Ask at the End of an Interview

Prepare for your data scientist interview with Coursera

Regardless of your experience level, preparing for your interview can help make it a successful one. By taking steps ahead of time, you can enter your next data scientist interview ready to showcase your knowledge.

On Coursera, you’ll find courses to start building your interview skills. In Big Interview’s The Art of the Job Interview, you'll explore proven techniques in five beginner-friendly classes that can help you turn your job interviews into job offers.

In IBM’s Data Scientist Career Guide and Interview Preparation course, you'll discover how to give an effective interview, including techniques for answering questions and how to make a presentation that is both personal and professional.