Interviewing a data scientist can be a difficult task. Candidates need the soft skills to get along with upper management and communicate effectively and the technical skills to collect, consolidate, and analyze information. Assessing candidates requires evaluating their technical and soft skills and personality to ensure they’re a good match for your company.
Revelo can help you source talent and line up interviews. We can even help coach you through interview preparation to make the hiring process seamless. But ultimately, only you can decide if a data scientist candidate is right for your company. This article provides valuable data science interview questions and answers for entry-, mid-, and senior-level candidates to help you make the most out of your interviews and ensure you’re hiring top data scientists to join your team.
What Is a Data Scientist?
Data scientists understand how to analyze various data sets and present the information to decision-makers in a way that helps them make critical business decisions based on data value; this role involves a lot of technical and analytical decision-making skills.
A good data scientist needs to do more than simply look at sets of particular data to predict trends. They must also understand what questions to ask, what type of data to collect, the nature of the data, and where to find it. Often, analysts need to provide an output for new data using several different platforms. As a result, data scientists need a combination of analytical and critical thinking skills.
Once they’ve compiled and analyzed new data, scientists must communicate their findings with upper management. Ultimately, companies use the data analytics that scientists compile to make major business decisions, so you want to be confident that the candidate you hire is the best one for your company. To learn more about data scientists’ responsibilities and qualifications, visit our in-depth data scientist job description guide.
Entry-Level Data Scientist Interview Questions
Entry-level data scientists generally work with mid-level and senior-level scientists to analyze data sets and manipulate a specific volume of data. Rarely will these entry-level workers have to know which questions to ask or which data to look for, and while they will need to communicate their results with other data analysts, they’re not yet communicating their results with upper management.
What you’re looking for from an entry-level data scientist is someone with a firm grasp of the technical skills needed to do the job. Assigning training data or enrolling the new hire in a data science course is always an option after employment, but you'll want someone who is highly coachable and motivated to learn. Ideally, this would be a stepping-stone position. Upper-level data scientists would eventually be able to train entry-level data scientists on the problem-solving and soft skills necessary to take on more responsibility.
Below are several questions to ask if interviewing an entry-level data scientist.
Can you write a program that prints numbers in a range from 1-100?
Although you’re not looking for programming expertise for an entry-level position, you do want to hire a candidate with a strong background in programming. Entry-level programmers should be able to write a basic program using Python or any programming language of their choice, plus provide clean data visualization. However, watch for basic mistakes early programmers can make. For example, if they write their range as range(100), the code will return numbers from 0-99; they must write range(1,101) to return the number of data points you’re looking for.
What is the goal of A/B testing?
Entry-level data scientists should be able to explain that A/B testing reduces guesswork and helps companies optimize products or websites when working with big data sets. This question can help determine whether your candidate understands the why behind their work, which is a great signifier about whether they have the problem-solving skills to continue moving up in this role.
How do you respond when offered constructive criticism?
Entry-level data scientists are still learning the ropes. Find candidates open to feedback and eager to learn on the job. Candidates who welcome constructive criticism are likely more coachable, which is much more important in an entry-level position than their starting knowledge base.
Mid-Level Data Scientist Interview Questions
Mid-level data scientists need to operate independently, perform exploratory data analysis, and may decide the best data to collect to answer different frequently asked questions. At this level, these specialists may work with data from multiple sources, so the need to clean the data increases exponentially due to the volume of data generated. They may also help train and supervise entry-level data scientists, making soft skills and behavioral interview questions for data analysts at his level more important.
Below are example questions to ask a candidate interviewing for a mid-level data-scientist role.
Can you explain how to make a decision tree?
By the time they’re interviewing for mid-level positions, data scientists should understand how the company uses the given data they analyze to make decisions and implement that knowledge on a simple level. When answering these questions, expect analysts who can tell you to do the following:
- Use the entire data set as an input, including using subsets of accessible data
- Determine which attributes to use to sort a set of data points based on which attributes provide the most information
- Continue branching data until you can parse it all based on those attributes and can make a final decision on each point in the data set
You receive a data set, but 25% of the variables are missing. What do you do?
Mid-level data scientists need to know how to problem-solve with imperfect or independent data sets. In this case, your data scientists should provide two answers: one for larger and one for smaller data sets.
In larger data sets, you may be able to remove the rows with missing variables and make your predictions based on the remaining rows. But if you have a small data set, you must fill in those missing variables using a mean average and data manipulation to get the best possible predictions.
What would you do if you realized a team member was using the wrong model to analyze a data set?
This question serves as a confidence check for mid-level data scientists. At this level, they should be able to recognize appropriate and inappropriate models for analyzing raw data sets. Ultimately, you’re checking to see if a candidate is a team player — in which case they should offer to help their team member use the appropriate model — or if they have a more insular mindset.
Senior-Level Data Scientist Interview Questions
Senior-level data scientists need an expert understanding of technical data analyzing skills. They must pair that understanding with fine-tuned soft skills to communicate their findings with upper management. They may also be in charge of a team of data scientists, so they need to have experience managing team members, understand how to train them, and provide solid leadership skills.
Senior-level data analysts are also responsible for organizing data collected by other scientists, performing data cleaning when needed, and ensuring a number of data sources have been analyzed appropriately before presenting it to upper management.
Here are some data science interview questions for experienced candidates that you can ask when filling a senior-level data scientist position.
What are the drawbacks to using a linear model when analyzing data?
Senior-level data scientists need to know more than which methods to use and how to use them; they also need to know why some methods are preferable to others. While most data scientists understand not to use a linear model for certain things, your senior-level specialist should know precisely what the drawbacks are and be able to explain those drawbacks in simpler terms, as they will need to coach junior-level data scientists on when to use this method and when not to.
At a minimum, senior-level data scientists should be able to explain that linear models don’t work for binary outcomes and that a linear model assumes a linear pattern of errors, which can lead companies to draw the wrong conclusions.
How would you explain your conclusions to a stakeholder with no technical knowledge?
Senior-level data scientists need to be able to explain nuanced, technical information in a non-technical way. Consider providing your candidates with a test data set and asking them to explain it as if you had no technical knowledge. Pay attention to whether they fluster easily, how much jargon they use, and whether they communicate the most critical aspects of the sample data analysis.
Can you give an example of a time you helped another data scientist reach their full potential?
You need to know your senior-level data scientists are team players capable of coaching junior-level data scientists. Here, you’re looking for answers that show your candidate helped coach a more junior data scientist rather than simply taking over a project for them.
Hire Data Scientists With Revelo
Hiring a data scientist can be challenging, especially for decision-makers who aren’t as technically knowledgeable as the applicants they’re interviewing. Generally, it’s a good idea to have a member of your data scientist team on a hiring committee to help you ask technical interview questions and vet candidates, but even then, finding the right candidate for your company can be difficult.
At Revelo, we can match you with highly qualified data scientists in your time zone. Our developers are rigorously vetted for both technical skills and soft skills. We support companies during the hiring process by managing payroll, benefits administration, taxes, and local compliance adherence. Feeling confident in your applicants’ abilities empowers you to focus on finding a candidate who will seamlessly integrate into your office culture.
Contact us today to learn more about hiring a top-tier data scientist.
