Data Scientist Interview Questions and Tips

Written by Coursera • Updated on

Explore this guide discussing what you can expect during a data science interview and example data science interview questions. You'll also learn how best to prepare for a data science interview, including tips on practice and job research.

[Featured image] Woman in an interview

Preparing for a data science interview is key to ensuring you perform at your very best, as preparation and practice will help to make you feel ready and more confident. In a data science interview, be expected to answer questions to show your capabilities in coding, data modeling, algorithms, and statistics.

Having some examples you can call on will mean that you're less likely to get stuck for an answer, and it will be a great review of your skills and experience. This article will present some examples of questions you can practice, tips for answering, and general interview support.

Coding and programming questions

For a data science role, questions about coding will be of high priority. Coding is an essential skill, regardless of the company you'll be working for, so an interviewer is likely to be mainly focused on this. These questions will typically involve data manipulation using code devised to test your coding, problem-solving, and innovation skills. 

Commonly, these will be tasks that you need to complete on a computer or whiteboard, or they may be questions that require you to talk through the problem and how you’d approach it. Here are some potential questions:

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

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

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

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

Data modeling techniques questions

After coding, questions on modeling are the next most likely category you'll face. The design behind the questions on modeling is to test your knowledge of building statistical models and implementing machine learning models. Examples include:

"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?"

Questions on algorithms

Questions on algorithms are primarily designed to test how you think about a problem and demonstrate your knowledge. Questions may take several forms:

"How would you reverse a linked list?"

"The recommendations, “People who bought this also bought…” seen on many e-commerce sites, result from which algorithm?"

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

"How often should an algorithm be updated?"

Statistics and probability questions

Questions on statistics in a data science interview look to test your knowledge of statistical theory and associated principles. They may include:

"What is the law of large numbers?"

"What is selection bias?"

"What is the process of working towards a random forest?"

"What is an example of a data type with a non-Gaussian distribution?" 

Questions on product sense and business applications.

These questions are specific to the business and how you would use data science. Answering these questions well can demonstrate your ability to apply your data science knowledge to a business capacity, rather than just understanding theory. Questions will likely be particular to the role, but use the following as a guide:

"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 we were looking 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?"

Tips for preparing for your data science interview.

“Practice makes perfect,” as they say. Making sure you are ready with some thorough practice is the best interview preparation you can do. Here are some ideas to help make sure you are ready for whatever comes up.

Research the position and the company.

If you want to know what may be asked in your data science interview, the best place to start is by researching the role to which you are applying, and the company itself. Check out company websites, social media pages, and reviews, and even speak to people who already work there if you can. 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 the easier it is to show that you are fully aligned.

By researching your role, you can better predict some of the questions you may hear. Go through the job description and see what is expected, as this is likely on what your evaluation will be based. Make sure you have an example prepared for each point and have a good bank of potential answers to any question. 

Understand the job description roles and responsibilities.

As you go through the job description and responsibilities to help you think of answers to questions, you should be clear on what will be expected of you if you get the job. If there is 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, it will be easier to tailor your answers and give highly relevant examples. You need to demonstrate that you can do the job, so it will be helpful to be clear on the prospective employer’s expectations for the candidate filling the role.

Practice answering commonly asked questions.

After finishing the research, and with some help from questions in this article, you should have some idea of what to expect in the interview. Write these questions down and practice your answers. It might feel strange, but the best way to do this is out loud as if you are talking to the interviewer. Doing it aloud means you can really hear how your answers sound and can practice your volume, speed, and body language.

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

The more you practice, the easier the answers will come to you. Interviews can be daunting, and it’s easy to forget the basics when put on the spot, but if you have practiced, you will be able to more easily recall information.

Have your 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.

Many 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 and show your interest in the position and company. Some examples of questions include:

• What is the metric on which my performance will be evaluated?

• 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 most immediate projects that need to be addressed?

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

Further support

For further help in preparing for a data science interview, look at what a data science job description may entail.

Be prepared for your interview by taking an online flexible course through Coursera. Big Interview’s The Art of the Job Interview presents proven techniques to help you make a success of your next interview.

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Written by Coursera • Updated on

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