If you’ve never heard of the STAR interview response method, it’s time to get acquainted. Research continues to show that soft skills are the number one employer priority even in the science fields, and with data science bleeding into other departments, those communication skills are critical.

Employers are getting better at defining what they’re looking for, even in technical fields, and data science interviews will continue to evolve. Let’s look at how mastering the STAR method can help communicate your technical skills and soft skills to potential employers. Competitive advantage achieved!

What is the STAR response?

Data science interviews are highly technical. You’ll get questions about data, visualization, the math foundation, and even the latest advances in a specific data science field — think NLP or transfer learning research.

Interviewers will also look for the soft skills you’ll need to fit into the team. These questions are open-ended and often cover situational experiences. Employers want to know how you behaved in the past with a particular situation to understand your thought process and your communication. They often ask you to imagine a situation or tell a real story from your past.

You can nail these sections and still fail to get the position because someone else could showcase softs skills throughout the interview process. So what’s an easy way to ensure you’re doing both?

For example: “Tell me about a time you improved a process.

To answer this question using the STAR method, you’ll paint a picture and then tell the story with a clear resolution.

STAR stands for:

  • Situation — Describe briefly the situation you found yourself in (“My team was losing a lot of time managing a poorly labeled data from two different data capture methods. Data was missing, duplicated with different labels, or incorrectly categorized.”)
  • Task — Describe your role or responsibility in this situation (“My support role as a data analyst involved taking ownership of this data labeling”)
  • Action — How you handled the situation. If you’re part of a team, highlight your specific efforts. (“My team and I built a data ingestion pipeline designed to streamline data capture while removing duplicate entries. I wrote a program specifically to check for missing data and prompt for completion.”)
  • Result — Highlight the outcome of this situation, focusing specifically on hard data if possible. (“Our team was able to shorten data scrubbing by 20% and reduce hidden errors by a third by the following quarter.”)

Common Data Science Interview Questions

Based on research and experience, we find these questions to be some common examples when it comes to the technical interviews for data science. Mastering the STAR response for interview questions ensures you’ll have a succinct, well-communicated answer.

  • How do you keep your technology skills current?
  • Can you tell me about a time a project failed or didn’t go as planned?
  • Tell me about a tech project you’ve worked on in your spare time
  • Can you tell me about your experience with a difficult stakeholder and what you did?
  • Can you describe a time when you disagreed with your boss/team?
  • How would you describe ABC skill to a nontechnical person?
  • What would you hope to achieve in the six months after being hired?
  • Can you tell me about a time you worked under pressure, and what was the outcome?
  • Why do you want to work for us?
  • Can you describe a time when you worked with another department to improve a process or achieve a goal?
  • What are your favorite and least favorite machine learning open-source frameworks, and why?
  • What strengths do you think are most important in a data scientist?

These questions are not exhaustive, but they all share one thing — asking for past experience or a thoughtful opinion. No right or wrong answers here, just uncovering your thought process and your emotional intelligence.

Practicing the STAR method

So how do you get comfortable framing your responses based on STAR? You practice. You build a set of experiences and opinions based on the underlying themes of these questions above. 

Practicing doesn’t mean lying or faking it. These responses are based on genuine projects, whether from a previous workplace, school, coding challenges or groups, or personal projects.

Make some notes for each question, and then begin to arrange that information using the STAR method.

For example:

  • Q: “How do you keep your data science skills current?” 
  • A: Jot down what you do. Make a plan of action using the specific situation, your task, your actions, and the results.
  • Q: “What strengths do you think are most important in a data scientist?”
  • A: Think of the most desirable skills as a data scientist. Now write down what your top skills as a data scientist. You’ll be able to highlight where your skills overlap with the ones on your list and develop a plan for filling in the gap. You can also outline which skills you’re currently pursuing.
  • Q: “Tell me about a data science project you’ve worked on in your spare time?”
  • A: This is a no-brainer. Start on your interests and highlight a portfolio of work on Github or coding challenges you worked on.

Practice with a friend both in person and over the video so that you get comfortable delivering these answers in these two interview formats. The appealing thing about STAR is that once you’re familiar with the method, it will help you formulate answers to curveball questions.

Take the time to practice and understand what employers want to see in terms of soft skills. STAR helps you highlight your expertise and knowledge, as well as your ability to communicate. It will level up your interview game.

Editor’s note: Ready to learn more in-demand skills, including data science interview prep, machine learning frameworks, and more? Subscribe to Ai+ Training and get access to hundreds of talks on-demand, live training sessions, and discounts to ODSC events!