Top 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

You've landed an interview for your dream job as a data scientist and are ready to show off your knowledge and expertise to the hiring manager. But, as a data-oriented professional, you know that the best way to improve your chances of success is by preparing in advance with practice questions and answers.

To help you put your best foot forward in your next interview, in this article you'll explore some of the most common questions posed to data scientists in job interviews and find tips for answering them. At the end, you'll also learn about some cost-effective, online courses that can that can help you ace your next interview.

Top data science interview questions

Preparation is key to ensuring you enter your next data science interview with confidence. Below, you'll find a list of some of the most common types of data scientist interview question on everything from coding and data modeling to algorithms and statistics.

Coding and programming questions

Coding is an essential skill for data science roles, regardless of the company in which you're working. As a result, interviewers are likely to ask you about your priori experience with such common programming languages as Python, R, and SQL. Typically, these questions will involve data manipulation using code devised to test your programming, problem-solving, and innovation skills. During the interview, you'll likely be required to use a computer or whiteboard to complete the questions, or you may asked to talk through the problem verbally and to explain your thought process. Here are some potential coding and programming questions you could be asked:

  • "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 data modeling techniques are ones you'll be most likely asked during your job interview. In particular, interviewers will likely want to know how familiar you are with different data models and their uses. Interviewers ask questions of this type in order 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. During your interview, here are some questions that you might encounter:

  • "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

Algorithms undergird much of the work that you'll be doing as a data scientist. Questions on algorithms are primarily designed to test how you think about a problem and demonstrate your knowledge. During your interview, consequently, you'll likely be asked to explain the purposes for different algorithms, how they might help solve different problems, and to demonstrate your knowledge of different machine learning algorithms. As a result, you should make sure to brush up on your knowledge of such common algorithms as linear regression and logistic regression. While the exact questions you'll be asked will vary from one interview to another, here are some of the most common forms they may take:

  • "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

Statistics are a cornerstone concept in data science. Unsurprisingly, then, interviewers ask questions about statistics in a data science interview in order to test your knowledge of statistical theory and associated principles. This is your chance to showcase your knowledge of 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. During your interview, questions of this type may take the following forms:

  • "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.

At the end of the day, most 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. In effect, you should expect to be asked how your work might contribute to the growth of the business and the development of the goods or services it sells. These questions are specific to the business and how you would use data science. Answering them effectively 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.

Thoroughly practicing for your interview is perhaps the best way to ensure its success. To help you get ready for the big day, here are some ways to ensure that you are ready for whatever comes up.

1. 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 try speaking 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 demonstrate that you are fully aligned with their 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 is expected, as this will likely be what you are evaluated on. Make sure you have an example prepared for each point and have a good bank of potential answers to any question. 

2. Understand the job description roles and responsibilities.

As you go through the job description and responsibilities for the position, try to get a clear sense of what will be expected of you. 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, 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 not only highlight your qualifications for the position but also the real world effects of your work.

3. 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 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 and 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

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?

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

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Further support

Regardless of your experience level, interviews can be nerve-wracking undertakings that have the potential to shake your self-confidence. Through preparation, though, you can enter you next interview with your head held high.

As you're looking for your next data science job, you might consider taking a cost-effective, online course through Coursera to get ready for your next interview. Big Interview’s The Art of the Job Interview, for example, will teach proven techniques in five beginner-friendly classes that can help you turn your job interviews into job offers.

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Written by Coursera • Updated on

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