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Data Science Interview Preparation

Find out how to prepare for a data science interview. Learn what to expect and how to approach the data science technical interview.
Dec 2022  · 12 min read

Congratulations, you have been invited to a data scientist interview! Your hard work in learning, building a portfolio, and applying for jobs has paid off, and now it is time to crack the data scientist interview. It is not an easy step, and you may have a lot of questions:

  • Do I need to do background research? How much time should I invest?
  • What about different types of interviews? Are they really very different?
  • What skills should I polish up before the interview?

Data science interview prep includes several steps, and we will go through all of them. After reading this article, you will have a complete toolkit to ace any data-related interview.

How to Prepare for a Data Science Interview: Background Research

First and foremost, do your homework and study the company, its industry, and your interviewers. This piece of advice is universal for any job interview but it is essential in the context of a competitive data job market. Here are the steps you can take to get to know your future employer.

Review the job posting

You've likely applied for a lot of different data science jobs (possibly through DataCamp's data jobs platform), so it's essential you revisit the specifics of the one you're interviewing for. Think about the requirements, skills you'll need, and any details of the company so that everything is fresh in your mind. 

Go to their website

Have a look at what products or services they offer. Select two of them and think of the ways you can enhance their functionality using your data scientist skills. During the interview, do not hesitate to share your ideas. Offer the company a few concrete ways your skill set will make it more competitive. By "selling" your value to the company, you dramatically increase your chances of getting hired.

Study their competitors

Research other industry players. What do they offer? What distinguishes them from the company you are applying to? It would be nice if you could come up with a great idea to outcompete their rivals and offer this solution during the interview.

Check the company’s values and culture

Do your values align with them? Do you like their culture? When briefly introducing yourself to the employer, subtly describe your life principles. Be honest with yourself and select only those values you truly believe in to make the right impression on the interviewers.

Find out the company’s recent achievements

Have they significantly increased customer numbers? Did they participate in an important conference? Use the company's website and social media, such as LinkedIn, Twitter, and Facebook. Take notes, and mention your findings during the interview. People like it when we talk about what they or their companies accomplished.

Research your interviewer

If you already know who will interview you, Google them, and search for them on LinkedIn. Find out what type of person they are. Do they contribute a lot to open-source projects? Volunteer at some social organization? Who knows, maybe you went to the same college? This knowledge will help to choose the best communication strategy to reach out to your interviewers.

Data Science Interview Prep: Interview Types

Cracking the data science interview means being ready for any interview type. An interview can be held with different technical means (such as phone, video call, or in-person) and with different company representatives (such as HR personnel, management, or data professionals).

Any data science interview

  1. Review your resume and cover letter because you will be asked about the things you wrote there. A glance at them will aid in predicting the questions you will face. 
  2. Be ready to ask questions about the company and the job. An interview is a dialogue where you can inquire about anything if you have any questions. It also shows initiative if you have some relevant and insightful questions to ask.
  3. Prepare your salary expectations. It does not matter if it is your first data-related job or if you have considerable experience in data science; you should know what salary to ask for. Check out Data Science Salary Expectations in 2022 to find out what to ask for your job title and skillset.
  4. Prepare the answers to the most common questions, such as: Why do you want to work here? What do you expect from your manager? Do you like to work more independently or in close contact with the team? 
  5. Conduct a mock interview with a friend. It is not the same as a real interview, but it offers great chances to spot any issue you have not thought of.
  6. Wear nice comfortable clothes. You want to feel comfortable while also reflecting the company culture and expectations. If it’s a corporate role, consider going smart. If it’s a trendy tech company, you can tone it down a bit. 
  7. Rest. Make sure you sleep well and are not exhausted or it will severely damage your chances of getting hired.

Phone interview

A phone interview is most likely the first type of interview you will go through. Although these can be daunting, by preparing ahead of time, you can reduce the stress:

  1. Ensure you have a good and stable connection. There should be no interferences or disconnections during the interview.
  2. Find a quiet place and tell everyone you will not be available for the next hour.
  3. Charge your phone. It’s very important that your cell phone does not die in the middle of your conversation.

Video call

A video call, in some ways, is similar to a phone interview, but in this case, you can see your interviewers.

  1. Have a stable and fast internet connection
  2. Install the necessary software for your video call
  3. Pay attention to your surroundings; they should be neat and professional.
  4. Your environment should be well-lit; test the video quality before your interview. Make sure your webcam is at a flattering angle.
  5. Check the webcam and microphone; they should work perfectly.

In-person

You might start with a phone or video interview before moving on to an in-person chat. Alternatively, you might have a few in-person interviews, depending on the role and company. Here are some tips: 

  1. Know how to arrive at the interview location. Plan the trip thoroughly and check how much time it needs to get there.
  2. Body language is important in face-to-face interaction. Practice beforehand. You should look like a friendly and open person.
  3. Do not be late but do not arrive too early. Coming 5-10 minutes before your interview slot is reasonable for most situations.

HR

An interview by an HR person is likely the first step in the data science interviewing process. They are generally interested in your psychological portrait and interpersonal skills rather than technical knowledge. The most typical questions to expect are:

  1. What are your strengths and weaknesses? This sounds like a simple question, but in reality, their interest is your ability to self-evaluate. Show them how your strengths will bring value to the company and how you plan to improve your weaknesses.
  2. Tell me about your biggest mistake; how did you fix it? Tell the HR a story. It should have a setting, a climax, and a conclusion. Tell them what you have learned from that challenge and how it helped improve your skills.
  3. How do you react to negative feedback? Show your professionalism by telling how you learn from it to enhance your work performance.

Preparing for the Data Science Technical Interview

Now it is time for a data science technical interview. Depending on the role, different skills may be required, such as SQL, Python, R, and machine learning. Here, we will cover a rather wide range of skills you may need and provide resources to master them.

Basic coding

As a data scientist, you will need to write code, most commonly Python, R, or SQL:

  1. Python. While applying for a data scientist job, have a quick review of the most common coding questions in Python, and check out our list of 23 data scientist interview questions.
  2. R. Practice some Machine Learning Interview Questions in R. It may be challenging, but it is a great learning experience!
  3. SQL. The Top 21 Data Engineering Interview Questions will give you an idea about the common data science SQL questions, and you can also check out our SQL cheat sheet. Learning how to make data-driven decisions in SQL is another crucial part of data science interview prep.

Statistics 

Statistics is required virtually everywhere. You do not need to know as much as a Master’s graduate in statistics, but you do need a good grasp of the fundamental concepts. Those include: mean, median, standard deviation, variance, normal distribution, statistical models, probabilities, Bayesian statistics, and hypothesis testing. You can test your statistical skills in Practicing Statistics Interview Questions in Python and Practicing Statistics Interview Questions in R.

Data cleaning 

Real-world data is unstructured and messy, so before you even start analyzing it, you should clean it. It is essential for any data role to be able to identify errors and inaccuracies in the data as well as to prepare it for statistical analysis and visualization. Refresh your data wrangling skills in our courses on Cleaning Data in Python and Cleaning Data in R.

Machine learning

Depending on your job description, it may be necessary to do some machine learning. Review the most common ML algorithms: linear and logistic regression, decision tree, random forest,  K-Nearest Neighbors, and then apply the skills by reviewing machine learning questions in Practicing Machine Learning Interview Questions in Python and R. Additionally, check out the top machine learning interview questions.

For a deeper understanding of machine learning, our course on Linear Algebra for Data Science in R is a good primer.

Data visualization

Data Visualization is arguably one of the most important skills for a data scientist. You should be able to present the work you have done in a comprehensive and clear way for both technical and non-technical audiences. If you need to talk to customers, the importance of this skill increases 10-fold. Be prepared to discuss the most efficient plots for different types of data. Check out these courses on the data visualization library ggplot2 for R and matplotlib for Python. Additionally, learn how to communicate data insights effectively.

Data Science Interview Tips and Tricks

Finally, let us learn a few tips and tricks that will assist you in cracking the data science interview:

No one expects you to know everything 

Not having a specific skill is normal. If the company asks for a solution in R, but you only know how to do it in Python, demonstrate how you can solve problems with Python and show your willingness to learn R.

Think before answering

Ask for more time if the question requires it. It shows that you take their questions seriously. However, do not do it for every question.

Explain the role of a data scientist

Sometimes, especially at smaller companies, they may not fully know why they need a data scientist. If this is the case, emphasize how you can improve the company’s visibility and profits by enhancing the existing products or creating new solutions.

Industries differ

Working as a data scientist in different domains may differ quite a lot. A biotech company is different from a cloud service provider. Spend some time to understand the specifics of the company’s domain and show the company that you want to learn. However, fundamentally anyone works with the data, and the data is approachable in similar ways no matter the industry.

Handling rejections

That is the reality of today's competitive job market. Learn from your mistakes, continue learning new skills, and improve the old ones. Ask advice from more senior employees, especially if they work in data science. You can also ask for feedback from the interviewer if you’re unsuccessful when applying for a role. 

Final Thoughts

Now you know how to prepare for a data scientist interview! Let us wrap up what we have learned:

  • Do background research on the company, industry, and interviewers.
  • Prepare differently for various types of interviews: phone, video call, in-person, with HR, management, or data professionals
  • However, all interviews are similar in many aspects. Make the suggested preparation steps a part of your routine.
  • Tons of data science resources are available to prepare you for any technical question. Check them all out regularly!

Data Science Interview FAQs

What should I expect during a data scientist interview?

A data scientist interview may consist of several different components, including a technical interview, a case study or problem-solving exercise, and a behavioral or fit interview. The interviewer may ask you questions about your technical skills, such as your experience with certain programming languages or machine learning algorithms, and may also test your problem-solving and communication skills.

How can I prepare for a data scientist interview?

To prepare for a data scientist interview, you should review the job description and requirements carefully, and make sure you have a strong understanding of the technical skills and knowledge that are required for the role. You can also practice answering common data scientist interview questions and prepare to discuss your experience, projects, and accomplishments in detail.

What are some common mistakes to avoid during a data scientist interview?

Some common mistakes to avoid during a data scientist interview include not having a thorough understanding of the job requirements, not being able to clearly communicate your skills and experience, and not being able to provide specific examples or evidence of your abilities. It's also important to avoid coming across as arrogant or unprepared, and to avoid making assumptions about the interviewer's knowledge or expectations.

What are some ways to stand out during a data scientist interview?

To stand out during a data scientist interview, you can focus on highlighting your technical expertise and experience, as well as your problem-solving skills and ability to work on complex projects. You can also demonstrate your enthusiasm for the role and show how your experience and interests align with the company's goals and values. Additionally, you can ask thoughtful questions and show a genuine interest in the interviewer and the company.

Data Science Interview Courses

Practicing Coding Interview Questions in Python

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