A Data Scientist uses the scientific method and applies it to data in order to get meaningful information from that data. This multi-disciplinary field combines techniques from mathematics, statistics and computer science. Learn more about how to pass a data science interview by preparing with the most common interview questions shared in this article.
Data scientist interview questions
Data science interviews include technical, practical and behavioral questions.
Here are some of the most common data scientist interview questions:
Why is it important to clean data before analyzing it?
Data Scientists need to be very precise for their work to be accurate. Interviewers will ask this question to see whether you understand that your data needs to be as high quality as possible before analyzing it.
Example: ‘Datasets often contain information that is outdated, irrelevant, incorrect or duplicated. As a Data Scientist, it is important to weed out the information I don’t need or want so that I can more accurately analyze and make better recommendations.’
Can you tell me the difference between bias and variance?
Fundamental data science concepts pop up quite frequently in Data Scientist interview questions. Many undergraduates have heard of these terms but only someone passionate about data will be able to summarize their difference on the spot. It’s a good idea to revise some simple definitions and make sure you know the differences between them.
Example: ‘Bias is the difference between the value a model estimates and the true value of that estimation, while variance is how variable or spread out the estimated model values are.’
Explain what a normal distribution is and why it is important.
Interviewers ask this question as a test of how well you know the most important distribution in statistics and to see how concise you can be when communicating technical ideas.
Example: ‘A normal distribution is a distribution of data that is symmetrical around a central value with no left or right bias. Normal distributions have a characteristic bell-shaped curve. The normal distribution is important because many physical, psychological and educational variables fit this distribution, which makes it possible to make accurate inferences about those variables.’
How does deep learning differ from machine learning?
Machine learning and deep learning are at the forefront of innovation in data science. While not every Data Scientist will have had the chance to work on such projects, interviewers want to know that you are taking steps to keep updated with industry developments. Again, showing your knowledge while also proving you can be clear and concise in your answers is important.
Example: ‘Deep learning networks can learn data features on their own, but machine learning needs manual feature extraction. Deep learning networks contain many hidden layers, while machine learning networks are much simpler, with one hidden layer between inputs and outputs.’
Tell me about a time you explained technical results to a non-technical person.
Behavioral questions are always good to practice because you can almost guarantee being asked one. Data Scientists often need to talk about complex ideas in business language so this type of behavioral question is a common one.
Example: ‘During a paid internship at my previous job, I had to segment customer behavioral data using factor segmentation. I created variables, cleansed and standardized the data and analyzed it. I told the marketing manager about a sub-group of customers that the business could target, and I suggested email marketing campaigns that would have a higher likelihood of converting them, based on how they interacted with the company’s website. Those recommendations improved profitability by three percent.’
Tips to answer data science interview questions
Here are some tips to help you prepare for a data science interview:
Revise technical definitions
It’s likely you’ll face several questions that test your working knowledge of the fundamentals of data science, including selection bias, logistic regression, time series and p-values. You can review previous college notes or textbooks because they’ll often have the best definitions.
Write your answers out to practice being concise
One of the best ways to impress in a data science interview is to communicate clearly and concisely. Practice actually writing out answers to the most common Data Scientist interview questions and try to refine those answers into as few sentences as possible.
Practice the STAR method for behavioral questions
Even though data science is a technical-oriented role, you will almost certainly be asked some behavioral questions. To answer a behavioral interview question, the classic STAR method is best. STAR answers behavioral questions by focusing on a situation, task, action and result. Draw from all of your experience to answer behavioral questions, including experience from college modules as well as work experience. Once you’ve thought of a real-world situation, be clear on what your task was, what action you took and what result you achieved.
Understand machine learning and AI concepts
Data Scientists increasingly use machine learning techniques to enable computers to learn from data. The Data Scientist then uses those insights for business purposes and translates them into simple language that all stakeholders can understand. It’s vital to understand exactly what machine learning is even if you have never worked with it.
Ask good questions
An interview is a two-way conversation and you will have the chance to ask questions at some point—don’t waste this chance. Ask good questions, such as what characteristics they think an employee needs to succeed and what the interviewer likes best about working for the company.