Interviewing for a new job can be intimidating. Potential hires are expected to know about the open position and their field of interest and convince the interview panel that they’re the right fit for the open role. For many data scientists, there may be the added challenge of live coding problems and technical questions.
It’s a competitive process. The average job opening receives hundreds of applicants, but only about 2% make it to the interview stage, according to a Glassdoor report on HR and recruiting [PDF, 281 KB].
But the competition can be worth it. The median annual wage for data scientists was $108,660 as of May 2021, according to the Bureau of Labor Statistics. That’s more than double the median annual salary for all occupations.
So, how do prospective hires move from one of hundreds of applicants to get an offer? Key factors include where you live and your level of experience. As you prepare for your data science interview, consider how best you can prove your skills. Take note that the more experience you have, the more leverage you may have to negotiate and share examples of how you put your skillset to use in real-world settings.
Navigate the resources below to become familiar with important skills, practice interview questions and more.
When interviewing candidates, hiring companies look for a variety of qualities to help them answer key questions and determine whether the candidate is the right person for the role. While each company’s set of questions and desired competencies vary, they typically want to answer the following questions:
Hard skills, or technical skills, include specialized knowledge and abilities. To assess your data scientist skills, prospective employers may ask you to take a test or complete a coding challenge. They are looking for your ability to solve the challenge as well as creativity, strategic thinking and code readability.
For examples of coding challenges, check out these websites:
These are just three of the many sites that offer coding challenges. Others you can explore for challenges and relevant resources include Coderbyte, Edabit, Codesignal, TopCoder, HackerRank and GeeksforGeeks.
When tackling coding tests, read the instructions carefully, sketch a plan and include a well-written “README” file. Make your test readable so another software developer can pick it up, read it and understand it without needing you to explain it. Brush up on your unit testing skills. Employers often look for your ability to write code and test it as well as learn and iterate on it, not necessarily to validate your coding.
If the challenge allows you to seek help and you need it, don’t hesitate to ask. Your prospective employer may be looking for your ability to ask for support and work as part of a team.
Before you submit your test, carefully review the results and check for errors. Be prepared for constructive criticism and feedback—this could be another litmus test that hiring companies use. How well do you take feedback? Do you defend your decisions, or do you ask questions and thank them for their input?
Coding challenges are as much about assessing your hard skills as they are your soft skills.
Soft skills are often more ambiguous than the testable data scientist skills, but that does not make them any less important. In a study of Fortune 500 CEOs, research showed that 75% of long-term career success depends on soft skills rather than technical skills.
LinkedIn data predicted the most in-demand soft skills for 2020:
When it comes to data science, experts in the field have noted a number of desirable abilities, including the knack for presenting data insights as a story would. In other words, you are able to weave your research and findings into a compelling, cohesive and easy-to-understand narrative. Other soft skills like strong written and oral communication, critical thinking, time management and sound decision-making may also be useful in your desired data scientist role.
You’ll want to show the interview panel that you possess such skills, rather than just telling them or listing them on your resume. As you prepare to go into your data science interview, it’s important to recognize your strengths and weaknesses. Evaluate yourself on the soft skills mentioned in the job description by thinking of examples of past work experiences where you demonstrated each one. Making note of such scenarios ahead of time can help you easily integrate them into your responses when the interview comes around.
Hiring managers may categorize their data science interview questions into three groups that allow them to:
The following sets of questions are examples of what data science job interviewers may ask. They are fairly detailed and may be helpful for preparation.
Technical questions are designed to test your hard skills and specific knowledge of the field. Potential technical data scientist interview questions include:
Practical interview questions are meant to help your interviewers discover how you work. Unlike technical questions, there are no correct or incorrect answers. Your interview panel will be looking for examples of how you have applied your knowledge, how you handle your mistakes, and how open you are to learn from your experiences. Consider the following questions and prompts during your interview prep and begin to think of specific examples.
These questions are used to get an idea of the nature of your working relationship with clients, co-workers and management. Your prospective employers want to know what you’re like to work with and how you connect with various stakeholders. Do you communicate effectively and respectfully? What is your preferred form of offering and receiving feedback? Likewise, questions and conversations about communication may help you understand the company’s culture. Here are few questions to think through:
Your interviewer is not necessarily looking for you to have expert knowledge in every facet of your field. They’re looking for your willingness to admit your shortcomings, ask for help and learn from your team. Instead of making up an answer or faking your way through a technical question, try some of the following responses to get more information and display an earnest desire to learn:
You can also try to turn a question back on the interviewer. Here’s one way to do that: “That’s one strength I hope to develop in the next few years. What is your approach to doing so?”
While it is helpful to study external resources, some of the best and most useful study materials may be ones you’ve made yourself. In the case of a job interview, your resume is a great resource. Interviewers should already be familiar with your resume and will likely ask you questions based on what you’ve included on it. Consider the following questions as you review your resume in preparation for your interview:
When a company lets you know that they are going to give you an offer, there may be room for negotiations. This part of the interview and hiring process may feel uncomfortable for some people. It can feel intimidating to reject an initial offer and suggest a counter one, not knowing what your prospective employer might say.
Do your homework and research salaries of data scientists. Keep in mind that geography, years of experience and the size and age of the hiring firm can affect how much salary you might earn.
If a hiring manager asks you for a salary range, you could say: “Based on my research, this is how much data scientists with my level of experience earn in this area. That said, salary isn’t the only factor that will make or break a job offer for me. I’m looking for a place where I can grow and make a real difference.”
You could also let the hiring manager know where you are in other interview processes. If that person gives you a deadline to respond to the offer and you are at various stages of the interviewing with different companies, try asking for an extension.
Your prospective employer might ask you to disclose previous inventions — codes, algorithms, software programs and models you may have written or contributed to — and they may ask you to sign an agreement that gives full or partial ownership to them for any inventions you create during the time you are employed with them.
In such instances, you might want to consult with an employment attorney, your academic counselor, a professor or mentor for advice. These people might also be able to help you fully understand your legal rights when it comes to signing pre-invention agreements and property and inventions agreements that are conditions for hire.
You may use the resource guides on our website to learn more about emerging careers for data scientists and explore online master’s degree programs in data science. You can also navigate information on related degrees, such as online master’s degrees in business analytics and computer science degrees.
Last updated: April 2022
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