Top Data Scientist Interview Questions and Tips

Written by Coursera Staff • 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. 

This article will explore some of the most common questions posed to data scientists in job interviews and provide tips for answering them. Finally, it will introduce you to cost-effective online courses to help you master your next interview. 

Top data science interview questions 

Preparation is key to ensuring you enter your next data science interview confidently. Below, you'll find a list of the most common types of data scientist interview questions, covering everything from coding and data modelling 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 will likely ask you about your prior experience with common programming languages like Python, R, and SQL. Typically, these questions 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 be asked to talk through the problem verbally and explain your thought process. Here are some potential coding and programming questions you could be asked:

  • "What would you do if a categorisation, 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 modelling techniques questions

After coding, questions on data modelling techniques will likely be asked during your job interview. In particular, interviewers will likely want to know if you're familiar 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. 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 regularisation 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, you'll likely be asked to explain the purposes of different algorithms, how they might help solve other issues, and demonstrate your knowledge of various machine learning algorithms. As a result, you should brush up on your knowledge of common algorithms such 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, resulting 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 cholesterol levels, 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, interviewers ask questions about statistics in a data science interview 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 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

Many employers are more interested in influential data scientists' impact on their bottom line than in exploring the field academically. In effect, you should be asked how your work contributes to the growth of the business and the development of the goods or services it sells. These questions are specific to the industry and how you use data science. Answering them effectively can show that you can 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 ensure 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 practising for your interview is the best way to ensure its success. To help you prepare for the big day, here are some ways to ensure 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 you are applying for and the company itself. 

Check out company websites, social media pages, reviews, and even speak to people working there. The more you can learn 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. Ensure you have an example prepared for each point and a good bank of potential answers to any question. 

2. Understand the job description roles, and responsibilities

As you review the job description and responsibilities, 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, tailoring your answers and giving highly relevant examples will be easier. Demonstrating the value you will add to the business with clear responses and concrete examples will highlight your qualifications for the position and the real-world effects of your work. 

3. Practice answering commonly asked questions

After finishing the research, and with some help from the questions in this article, you should know what to expect in the interview. Write down these questions 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. Doing it aloud means you can 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. 

Have your questions ready

While it’s important to think 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 to learn more about the role, decide whether it is for you, and show 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?


Further support

Regardless of your experience level, interviews can be nerve-wracking undertakings that have the potential to shake your self-confidence. Preparing for an interview makes you confident and allows you to give your best.

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

Keep reading

Updated on
Written by:

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.