Common data science interview questions and answers

Typically, potential employers will want to conduct at least one interview before hiring a candidate for a data science job. Here are some common data science interview questions as well as interview tips to help candidates prepare.

Before delving into possible data science interview questions, candidates will want to research the company where they are interviewing. Visiting the organization’s website and LinkedIn profile will reveal more about the company’s:

• History
• Customers, services, and products
• Mission statement
• Competition
• Latest press
• Employee culture

Candidates should also carefully review the job description for which they are applying, noting how their experience matches role requirements. They may even want to make note of such alignments so they can remember to showcase relevant data science skills and attributes during the interview.

Interviews can happen in a variety of formats, including:

• Phone: Introductory call to verify experience and salary expectations
• One-on-one: This can take place in person or via videoconference
• Panel: Includes multiple interviewers and a single candidate
• Group: Several candidates interviewed together
• Working: An evaluation during which the candidate performs job-related tasks

Interviews may start with simple questions about the candidate’s background that are an opportunity for candidates to present themselves in the best light.

The company wants to see how candidates introduce themselves, organize their thoughts and highlight key achievements.

How to answer: Talk about accomplishments as they relate to the job posting, and don’t be afraid to show some personality while answering.

Sample answer: “I’m a senior data scientist with more than six years of experience building and deploying machine learning models that drive revenue and optimize operations. In my current role, I recently spearheaded a customer churn prediction model that improved retention by 10% and saved the company about $300,000.”

Hiring managers ask this question because they want to see whether a candidate has researched the position and genuinely considered how they could bring value.

How to answer: Candidates should mention specific things they appreciate about the company, detail why the specific tasks associated with the role excite them and explain how their background will help the company reach its goals.

Sample answer: “I want this job because I see a great opportunity to make a tangible impact. I’ve closely followed your company’s recent expansion, and I know my background will allow me to help streamline your processes.”

Interviewers want to gauge a candidate’s ambition and long-term commitment and determine whether their career goals align with the role and company.

How to answer: Answer this question by focusing on the desire for skill development and growth within the company. You can also mention how your goals align with the company’s mission.

Sample answer: “My goal is to become a true subject matter expert in this role. I want to deepen my technical and strategic understanding so I can consistently deliver great results and help the team achieve its larger objectives.”

As the interview progresses, questions will likely become more technical.

The interviewer wants to test whether the candidate understands model evaluation, generalization and the bias-variance trade-off.

How to answer: Candidates should show they can provide more than a textbook definition and can connect foundational concepts to professional situations.

Sample answer: “Cross-validation doesn’t directly change a model’s bias or variance, but it helps us estimate how a model will perform on unseen data. By training and testing the model on multiple data splits, we get a more reliable estimate of its generalization error than with a single train-test split.”

Hiring managers want to find out if a candidate knows and understands common classification metrics.

How to answer: Explain that the choice of metric depends on the business problem and class balance.

Sample answer: “The right evaluation metric depends on the problem, especially whether the classes are balanced and what type of errors are most costly. Accuracy is a good starting point, but it can be misleading for imbalanced datasets.”

Coding and data wrangling are important parts of a job in data science, and a candidate may get questions on such topics.

This question is testing the candidate’s SQL skills.

How to answer: First, explain the concepts individually. Then, demonstrate them with an example.

Sample answer: “You can combine aggregation, categorization and ratios in the same SQL query by using GROUP BY for aggregation, a CASE statement for categorization, and arithmetic on aggregate functions to calculate ratios or percentages.”

Interviewers may want to find out how candidates will approach building large-scale machine learning solutions in specific case studies.

This is a real-world situation that may very well come up in the job.

How to answer: First, validate the metric to make sure it’s correct before embarking on the solution. Then, talk about the process of segmenting the data to see where the decline is happening. Next, investigate possible explanations and solutions and form a hypothesis.

Sample answer: “I’d first validate the metric, then analyze when the drop began, segment users to identify affected groups, investigate recent product changes, and examine related engagement metrics to determine the root cause.”

It’s important not only to come up with a solution, but also to know how to frame it for stakeholders.

How to answer: Give a step-by-step process to follow in a hypothetical situation.

Sample answer: “I’d begin by documenting the business requirements, defining clear success criteria, outlining my analytical approach and presenting a validation plan. Then I’d summarize the key findings, recommendations and how I’d measure the solution’s effectiveness.”

Behavioral data science interview questions evaluate a candidate’s communication skills, stakeholder management strategies, and problem-solving under ambiguity.

The interviewer wants to know whether the candidate just follows orders or can come up with their own solutions.

How to answer: Use the STAR (situation, task, action, result) method to give the background details of an event, explain what goal you needed to reach, describe the steps you took to reach that goal and share the final outcome.

Sample answer: “In one role, I noticed our team was spending several hours each week manually cleaning and combining data. I wanted to make that process more efficient and reduce the chance of errors. I automated the cleaning and aggregation and added a few validation checks. The result: We cut the process from several hours to about 30 minutes, with fewer errors and more time for actual analysis.”

When asked data science interview questions, candidates may need to share how they addressed a work-related challenge. It gives candidates a chance to highlight their strengths.

How to answer: Again, the STAR method is helpful. Be sure to keep several stories top of mind before the interview; challenging situations are always good fodder for an interview story.

Sample answer: “I was working on a project where the data I needed was incomplete and inconsistent. I still needed to deliver a reliable analysis by the deadline, so I identified the gaps, worked with the relevant teams to clarify the data, and created a process to validate what I received. As a result, I was able to complete the analysis on time and provide results the team could confidently use.”

Interviewers often ask candidates whether they have any questions; don’t pass up the opportunity to learn as much as possible about the position and the organization.

Consider asking:

• What is the structure of the team I will be working with?
• What are the metrics for performance measurement?
• What are the company’s expectations for my first 30 to 90 days?
• Are there opportunities for advancement within the company?

When preparing for the big interview or series of interviews, keep the following tips in mind:

• Consider connecting with someone in the field but not with the company in question to inquire about what might be helpful to ask about during the interview.
• Be mentally prepared to work with messy data.
• Follow up with a thank-you note.

One way to prepare for data science interview questions is by developing your skill set through education. University of Phoenix offers a variety of technology programs that teach relevant data science skills, including:

• Bachelor of Science in Data Science
• Master of Science in Data Science

Contact University of Phoenix for more information.