No, It’s Not the “Easy” Round
in on interviews at my previous company, I used to think the behavioral part of the interview was the easy part. That if someone was technically proficient and could wow the interviewers with their analytical mind, they’d be the top contender for any job.
Then I witnessed a highly technically skilled applicant lose out to a more socially competent one.
Not because he lacked experience. He had done good work, and he obviously knew what he was doing. He just had no idea how to tell us about what he’d done in a way that landed, and how to connect his work as a data scientist to the things my team actually cared about: collaboration, communication, and decision-making under uncertainty.
Here’s the thing about behavioral interviews for data science roles specifically: they’re different from behavioral interviews for other fields. Companies aren’t just checking if you’re a nice person. They’re figuring out if you can translate technical work into business value, manage relationships with non-technical stakeholders, and handle situations where the data doesn’t give you a clean answer.
Here are 3 tips I’d give anyone before a behavioral interview.
1. Treat Every Story as a Stakeholder Communication Problem
The biggest mistake I see data scientists make in behavioral interviews is telling the technical story when the interviewer wants the business story.
You’re asked: “Tell me about a time you had a difficult project.” You launch into a detailed explanation of your cross-validation approach, the hyperparameter tuning you did, the precision-recall tradeoff you navigated.
The interviewer’s eyes glaze over.
Here’s what I’ve learned, both from my own interviews and from watching other data scientists navigate their careers: at most companies, the data scientist who can explain their model’s business impact in plain English is more valuable than the one who can explain the math better. Your interviewer doesn’t need the technical deep-dive. They need to know:
- What was the problem?
- What did you do?
- Why did it matter?
I wrote about this exact challenge in my article on working with stakeholders: A Data Scientist’s Guide to Stakeholders
Before your interview, practice framing your stories using this structure:
- What was the business problem (not the technical problem)?
- Who was affected or involved?
- What was your contribution, in plain language?
- What was the measurable outcome?
Instead of saying “I built a time series forecasting model using lag features and Random Forest that reduced RMSE by 40%,” try: “We had a recurring issue where our team was over-ordering energy resources by a wide margin every month, which had real cost implications. I built a forecasting model that gave us a more accurate week-ahead prediction, which directly cut our overage costs.”
2. Do Your Research
I recommend starting with a basic search on Google: “[Company Name] behavioral interview questions”. You may find information on Glassdoor, Reddit, and other smaller websites. For larger companies especially, you’ll often find threads where past candidates share the actual questions they were asked, what the format looked like, and how the process felt. Keep in mind that teams change their questions over time, so don’t treat old reviews as gospel, but they’ll still give you a strong sense of what the company values and how they like to probe for it.
You can also look up a general list of behavioral interview questions for your specific role (Data scientist, data engineer, data analyst). A data scientist might get asked more about ambiguous projects and model trade-offs. A data analyst might face more questions about communicating findings to leadership.
Search for YouTube videos of behavioral mock interviews or people who have conducted many rounds of data science interviews. Seeing how someone else answers will teach you more than reading a list of tips. Pay attention to:
- What situations the candidate brought up, and what similar ones you’ve been in
- The candidate’s facial expressions and overall demeanor
3. Prepare a Few Situations Ahead of Time
A lot of behavioral interview prep advice focuses on conflict: “Tell me about a time you disagreed with a colleague” or “Describe a situation where you failed.” Those questions matter, but for data science roles, the harder category is ambiguity.
- “Tell me about a time you had to make a decision without having all the information you needed.”
- “Describe a project where the requirements changed partway through.”
- “How do you handle situations where the data doesn’t support a clear answer?”
These questions are specifically designed to assess something that matters a lot in data science: your tolerance for uncertainty and your ability to move forward without perfect information.
The best way to plan for these is by using the STAR method.
STAR stands for:
- Situation: What was the context/background?
- Task: What were you specifically tasked with doing/solving?
- Action: What steps did you take to solve the problem?
- Result: What was the outcome?
Let’s walk through a specific example of the STAR method: “Tell me about a time you had to make a decision without having all the information you needed.”
Situation: Midway through a forecasting project, I discovered that two months of historical energy consumption data had been logged incorrectly due to a meter error in the middle of the training window I was planning to use.
Task: My stakeholders needed a working model delivered by end of sprint. I had to decide whether to delay the project to investigate the data issue further, or proceed with a modified approach and flag the risk.
Action: I trimmed the affected window from the training set, retrained on the cleaner data, and ran a quick analysis to quantify how much predictive power I was likely losing. I brought both options to my stakeholder (delay with more certainty, or deliver on time with documented caveats) and let them make the call with full information.
Result: We were able to deploy the model on time. We achieved a 12% reduction in mean absolute error compared to the existing baseline, and our week-ahead forecasts were accurate enough to reduce energy over-ordering by ~18% in the first month of deployment. The stakeholder later told me the transparency about the data issue actually increased their confidence in the results, not the other way around.
Take the time to write some notes about these examples (and more) down on paper. That way when a question comes up, you’re not caught off guard. Even if it’s a different question than the scenarios you originally planned for, having a few adjacent scenarios you can pull from is still much better than having a blank mind in the moment.
Conclusion + Bonus Tip
In my first year as a data scientist, I learned that the job is rarely about finding the perfect answer. It’s about finding a defensible one, fast enough to be useful. Stakeholders don’t wait for perfect data. Business decisions have deadlines. The ability to say “here’s what the data supports right now, and here are the assumptions I made” is a skill in itself.
So before your interview, think about moments where you:
- Delivered a recommendation before the model was perfect
- Identified that a project had changed scope and adapted
- Made a judgment call and owned the consequences
- Communicated uncertainty clearly rather than hiding it
And write down a few of these situations before your interview. That way, they’ll be fresh on your mind.
Here’s a final bonus tip: Remember to smile, keep it light, and have a good attitude. This can make a much bigger difference in your interview than you think. Try to make small talk with your interviewers. Find something you have in common with them. Don’t be afraid to crack a light joke. You would be surprised how far this can take you and make you stand out above other candidates.
