According to the Bureau of Labor Statistics, employment of data scientists is expected to grow a staggering 35% between 2022 and 2032, much faster than the average projected growth for all professions.
That means that not only will a job in data science reward you well financially, but it will also open the door to many prospects, allowing you to enjoy healthy job security.
These characteristics, however, make the field a highly competitive one, meaning you often have to work hard to stand out and make a good impression on hiring managers, both with your résumé as well as in person during interviews.
To help you prepare, we’ve compiled a list of 20 interview questions you can expect to encounter, covering general and non-technical questions as well as industry-specific ones.
General questions
General questions are usually posed at the start of the interview and help break the ice a little.
1. “Tell me about yourself.”
Despite its being a straightforward prompt, “Tell me about yourself” can be challenging to answer concisely and effectively if you haven’t prepared for it.
To avoid going off on tangents, keep the job listing and company culture in mind. By doing so, you can relate your answer to what the interviewer is looking for in terms of qualifications, skills, achievements and cultural fit.
SAMPLE ANSWER
I recently graduated from the University of Pennsylvania, where I completed an MSE in data science. It has been my goal to work in policy making since my second year of college, when I understood how big of a positive change we can make in our communities when we ethically harness data.
2. “Why did you pursue this career?”
If you hear this question, now is your chance to convey your enthusiasm about the profession and industry.
If the main reason you chose data science was the salary prospects, it’s fine to mention financial security in passing, but perhaps avoid making it the entire focus of your answer. Instead, pick a few more things to talk about that can give the hiring manager a glimpse into your personality and interests.
SAMPLE ANSWER
From a young age, I have been passionate about artificial intelligence. I have also always enjoyed solving problems and uncovering patterns. Data science happened to combine these, challenging me and motivating me to work hard and try to contribute as much as I can to the field of machine learning.
3. “Why do you want to work for us?”
Your prospective employer isn’t fishing for compliments — although mentioning something you admire about the company can be good. Instead, what they want to see is that you have done your research, that you understand their mission and values, and see yourself making a great cultural fit.
SAMPLE ANSWER
The thing that stood out to me the most about ABC Cybersecurity, besides its long history, was its mission: to protect clients from threats, all the while striving to reduce your environmental impact. As it happens, I am also deeply passionate about cyber sustainability.
4. “What is your biggest accomplishment?”
Regardless of the role you’re interviewing for, this is one of the most commonly asked interview questions. Although it can feel a little unnatural talking yourself up, it’s an important part of the process when you’re looking to land a job.
A good tip for answering this question is to use figures, such as percentages and dollar amounts, when discussing results you have achieved.
SAMPLE ANSWER
One thing I took the initiative for in my previous role was organizing a fundraiser event for Doctors Without Borders. Working as a data scientist in healthcare, I often think about those who have no access to medicine or care, and so I teamed up with others from my department and raised $4,000.
Behavioral questions
These questions help the interviewer assess your suitability for the role by revealing your behavioral patterns, such as how you handle stress and address problems.
5. “Describe a project you worked on with a difficult coworker. How did you handle it?”
Behavioral questions (often starting with “Describe a time when…”) are typically answered using the STAR interview method or a similar technique.
This question in particular can reveal how good of a team player you are and how you navigate differences between yourself and your colleagues.
SAMPLE ANSWER
I once worked on a project with a colleague who had a different approach to problem solving than me. I was more data-driven, while they had the tendency to jump to conclusions before conducting a careful analysis.
When friction inevitably arose, I decided to remind them (and myself!) of our common goal and took the time to listen to their perspective and acknowledge their strengths, to try and align our approaches as best as possible. By acknowledging and discussing our differences, we were able to create a robust model.
6. “Have you ever made a mistake in your analysis? How did you navigate the situation?”
Mistakes are inevitable. Knowing how to effectively deal with mistakes, therefore, is vital in progressing your career and minimizing the impact it has on your team and employer.
Hiring managers ask this question to gauge some desirable soft skills including your sense of accountability, adaptability and honesty, to name a few.
SAMPLE ANSWER
When I got my first-ever job, I overlooked a data preprocessing step which caused me to misinterpret the performance of the model. Although identifying the problem and rerunning the model took some time, it made me a lot more cautious in checking each step of my data pipeline.
7. “Have you ever led a team? What was the most challenging aspect of it?’”
This question can give the interviewer insights into your leadership ability as well as your interpersonal skills, such as your collaboration skills and cultural awareness. If you haven’t led a team before, talk about a time you showed initiative or voiced your ideas, expanding on the impact that it had on your team.
SAMPLE ANSWER
In my previous role, I had to guide a small data science team, aligning our efforts with business objectives and ensuring ethical practices. I would say that the biggest challenge was communicating effectively with people from different cultures and backgrounds. I had to consciously remember to listen actively to each member of the team, and bear in mind the differences in communication styles.
8. “Have you ever faced an ethical dilemma around data usage? What did you do about it?”
By following ethical practices, data scientists can remain compliant with regulations as well as contribute to making a positive impact on society. Your interviewer wants to verify that you know this and honor it in your work!
SAMPLE ANSWER
I once worked on a model for predicting the likelihood of customers defaulting on loan payments. Because we were feeding people’s personal data into the model, such as their zip codes and ethnicities, there was a likelihood of bias occurring. To mitigate this, I called a meeting with my team to carry out a stringent bias analysis, which eventually led to us removing some variables from the model.
Non-technical questions
Non-technical interview questions are designed to assess your soft skills, such as your interpersonal ability, showing the interviewer how you think, behave and react to challenges.
9. “How would you explain data science to someone with limited knowledge of the subject?”
This question can give the hiring manager a glimpse into how good of a communicator you are. Even though you could possibly give a lengthy, complex answer, what they are looking for here is the ability to condense information and deliver it in an easy-to-follow way.
SAMPLE ANSWER
Data science is the process of studying data and trying to uncover patterns from it. For example, the data could be in the form of individual satisfaction scores from a customer service survey; the more closely you analyze that, the more targeted improvements you can make.
10. “How do you stay up to date with the latest advancements in data science?”
The world of data science is extremely fast-paced. Lifelong learning, therefore, is essential to your professional success — and, as a result, to that of your employer. Acknowledge the fast-paced nature of the field and the need for staying up to date when formulating your answer.
SAMPLE ANSWER
To ensure I get the latest industry news, I have subscribed to newsletters such as Data Science Weekly and Data Elixir, and also follow industry leaders on LinkedIn, YouTube and other social networking platforms. Once or twice a month, I also dedicate time to visiting blog posts, such as that of Chip Huyen.
11. “How do you prioritize your workload when you have multiple deadlines approaching?”
All of us, regardless of profession, experience demanding periods where our to-do lists appear never-ending. What you want to do when answering this question is demonstrate a logical, calm approach to prioritizing, even when you’re swamped with work.
SAMPLE ANSWER
Typically, I prioritize high-impact projects as well as project dependencies. During periods when my workload is more demanding, I run my prioritization decisions by my boss to make sure they’re on board with what I’m doing.
12. “How do you see your role evolving over the next few years?”
By asking this question, the hiring manager wants to see if your future aspirations and expectations align with what they can offer. By expressing what you envision, you also demonstrate your professional drive and focus.
SAMPLE ANSWER
Over the next few years, I would like to see myself lead a team, as I believe it’s very meaningful to hold a mentorship role. Motivating others towards achieving a common goal while also providing assistance where needed will be challenging — albeit in a very positive way!
Technical questions
Unlike non-technical questions, which provide insights into your soft skills and personality, technical questions are asked to assess your hard skills and industry-specific knowledge.
13. “What programming language are you most familiar with?”
To answer this question effectively, bring the job listing to mind. What were the requirements for the role? If Python was one of them, for example, lead with that when providing your answer.
SAMPLE ANSWER
I have used Python and SQL extensively in my career. I also have some experience with R, although that’s more limited.
14. “Can you walk me through your qualifications?”
You might wonder why the interviewer might ask something like this when they have your résumé right in front of them. In part, they want to test your ability to structure your thoughts and communicate concisely. They might also want to see the reasoning behind some of your certifications.
SAMPLE ANSWER
Absolutely. I completed a bachelor’s degree in computer science at the University of Virginia and then did the UC Berkeley online master’s degree in data science. Last month, I completed the Microsoft Azure certification as well, as it is widely used in the financial services sector.
15. “Can you define univariate, bivariate and multivariate analysis?”
These types of questions are more likely to crop up if you’re applying for a junior position or don’t have a lot of relevant work experience. The purpose is for the hiring manager to determine how comfortable you are with concepts and terminologies used in the field.
SAMPLE ANSWER
In simple terms, univariate analysis entails analyzing one variable, such as the weight of a group of people to calculate the average. Bivariate, meanwhile, entails analyzing two variables, and multivariate three or more variables.
16. “What is the main advantage of sampling?”
Much like the previous one, this technical interview question might be posed to recent graduates and applicants with limited relevant work experience.
SAMPLE ANSWER
Sometimes, when datasets are large, analysis can’t be performed on the entire volume of data. Sampling allows us to analyze smaller samples of said data so we can arrive at conclusions that represent the whole, without actually having to analyze the entire dataset.
Advanced data science-related questions
Industry-specific questions can range from asking you to provide simple definitions to testing your knowledge on a deeper level. Let’s look at some more complex questions you might receive.
17. “What are some techniques to prevent overfitting?”
Overfitting in machine learning occurs when a model gives accurate predictions for training data but not any new data you might input. As such, being able to remedy this is essential. The more specific and concise your answer, the higher levels of confidence you can convey in your interview.
SAMPLE ANSWER
Some ways of preventing overfitting include using cross-validation, gathering more data, simplifying the model and pruning decision trees.
18. “Can you walk me through the steps of creating a decision tree?”
Whether you work in machine learning, statistics or data mining, decision trees are a useful tool for modeling the relationships between different variables. Like the previous question, this one tests both your knowledge and your ability to convey information concisely and with confidence.
SAMPLE ANSWER
To create a decision tree, you first select and preprocess the dataset. Then you split the data into training and testing sets, selecting a splitting criterion. After building the tree, you can also check if it needs pruning to increase its predictive power.
19. “How do you deal with missing values in a dataset?”
Knowing how to deal with missing values is critical in ensuring reliable model predictions. Being a vital bit of knowledge, you can expect to hear a question on missing values during your interview.
SAMPLE ANSWER
If the impact is negligible, one way would be to completely remove the rows or columns with missing values. Another way of dealing with missing values is to fill them in, for example by calculating the mean or median or by using predictive modeling.
20. “How would you detect anomalies in a high-dimensional dataset?”
It’s common for data scientists to encounter high-dimensional datasets in fields such as finance, healthcare, marketing and manufacturing. Given their commonality, you can expect the topic to pop up in an interview.
SAMPLE ANSWER
One way would be by using a technique like PCA to reduce dimensions, which makes it easier to identify anomalies. Another way would be through clustering; for example, a DBSCAN can also help identify outliers.
Final thoughts
Job interviews can be stressful, but when you prepare, the experience becomes significantly less nerve-wracking. By reading interview guides like this one, which includes questions for data scientists of all levels, you can go to your interview feeling a little more confident and relaxed!
And when you’re able to convey a strong sense of professional self-esteem, hiring managers are likelier to invest in you.
So, there we have it: the top interview questions for data scientists! Can you think of any more to add to our list? Let us know in the comments section below.
This article is a complete update of an earlier version originally published on September 11, 2019.