Data Scientist Interview Questions

Data scientists have highly technical roles that involve knowledge of advanced mathematical theories and data analysis techniques. As a result, it’s important to assess each candidate with a wide range of interview questions. A balanced interview will include questions about general data science methodologies, plus specific situations candidates may encounter in their work.

Need inspiration? Keep reading to get sample interview questions for data science that you can use as a guide when reviewing candidates!

What Qualifications Does a Data Scientist Need?

Data scientists build machine learning models and algorithms to help categorize and interpret data for different business uses. To perform their job duties, these experts leverage technical skills like:

Data cleaning: Data scientists must know how to clean and process raw data into a readable format to interpret it correctly.
Programming: Because data scientists spend a significant amount of time writing algorithms, they must have a strong background in writing code in languages like Python, R, and SQL.
Database management: Organizing, classifying, and accessing data in massive databases is essential for building functional data models.
Statistical analysis: Data scientists leverage their knowledge of statistics to create accurate predictive models and identify relevant trends for their projects.

Data scientists also use soft skills like problem-solving and critical thinking to apply their skills in real-world business environments. Asking questions that target these skills can give you the information you need to evaluate each candidate’s ability to thrive on your team.

Specific Interview Questions for Data Scientists

When interviewing candidates for a data scientist role, start by asking general interview questions about their education, background, and career ambitions. Then, transition into more specific questions that target data science, data analysis, statistics, and other high-level topics.

Here are 20 data scientist interview questions—broken up by topic—that you can use to guide the skills-based portion of your interview:

Data Preparation Questions

Before data scientists can begin writing algorithms, they need to collect and prepare their data. Try asking these questions to learn more about how they manage data for their projects:

What is data normalization, and what are its main rules?
How do you identify which datasets are relevant to a project?
Describe your process for cleaning data.
How do you approach projects with large amounts of raw data?
Tell me about a time when you worked with corrupted data in a dataset.

Coding Questions

Some data scientist interviews involve coding tests where candidates write code in response to various prompts. You can also learn more about their coding ability by asking these questions:

Which Python libraries do you use most regularly, and why?
When would you use R instead of Python during the data analysis process?
Describe how you would approach building a data pipeline.
Explain how SQL sub-queries work.
Tell me about a time when you used data manipulation.

Data Analysis Questions

Depending on the type of problem they’re trying to solve, data scientists may apply several different data analysis techniques. Ask these questions to assess their experience with data analysis:

When would you use a decision tree, and what’s the process for creating one?
How do you evaluate the quality of a regression model?
What are the characteristics of an effective data model?
Tell me about a time when a data model you created was unsuccessful. How did you identify and address the issue?
Describe how you use statistics to guide your analysis process.

Machine Learning Questions

Machine learning is central to modern data science. Ensure that your candidates are qualified by asking these machine-learning questions:

How do you determine which machine learning model to use?
Describe your experience working with neural networks or deep learning.
How do you use the k-means clustering algorithm?
How does the random forest algorithm work, and when would you use it?
Describe your approach for addressing confounding variables in machine learning projects.

Hire Skilled Data Scientists with Insight Global

Data scientists enable businesses to make strategic, data-based decisions. Asking thoughtful interview questions can help you find qualified data science candidates who can apply their knowledge of statistics, programming, and machine learning to improve your organization.

By carefully assessing each candidate’s responses, you can select the best possible match for your team and improve your overall data science practices.

Need more interviewing guidance from the experts? Download our Complete Guide to Interviewing Candidates. In this guide, you’ll discover:

Different types of interview formats
Types of interview questions (with examples)
Common interviewing mistakes
Interviewing advice from experiences staffing experts
And so much more

Or, if you want more hands-on help, reach out to Insight Global today. We’re a full-service recruiting and talent management agency and the second largest IT staffing company in the U.S.—we can help you find your next great hire in as little as two days.