No, It's Not an “Easy” Cycle
in interviews at my previous company, I used to think that the behavioral part of the interview was the easy part. That if someone has technical skills and can impress the interviewers with their analytical mind, they can be a top contender for any job.
Then I saw a highly skilled applicant lose to a socially skilled one.
Not because he lacks knowledge. He had done a good job, and he obviously knew what he was doing. He just didn't know how to tell us about what he had done the way it came, and how to connect his work as a data scientist to the things my team really cared about: collaboration, communication, and decision making under uncertainty.
Here's the thing about ethical discussions for data science roles specifically: they're different from ethical discussions for other fields. Companies don't just check if you're a good person. They consider whether you can translate technical work into business value, manage relationships with non-technical stakeholders, and handle situations where data doesn't give you a clean answer.
Here are 3 tips I would give anyone before a behavioral interview.
1. Treat Every Issue as a Stakeholder Communication Problem
The biggest mistake I see data scientists make in behavioral interviews is to tell a technical story when the interviewer is looking for a business story.
He is asked: “Tell me about a time when you had a difficult job.” You go into a detailed description of your cross-validation method, the hyperparameter tuning you've done, the recall-recall off tradeoff you've navigated.
The interviewer's eyes sparkled.
Here's what I've learned, both from my conversations and from watching other data scientists navigate their careers: in most companies, a data scientist who can explain the business impact of his model in plain English is more valuable than one who can explain statistics better. The interviewer does not need technical depth. They must know:
- What was the problem?
- What did you do?
- Why was it important?
I wrote about this challenge specifically in my article on stakeholder engagement: A Data Scientist's Guide to Stakeholders
Before your interview, practice structuring your stories using this structure:
- What was the business problem (not a technical problem)?
- Who is involved or involved?
- What was your contribution, in simple terms?
- What was the measurable result?
Instead of saying “I built a time series forecasting model using sleep features and a Random Forest that reduced the RMSE by 40%,” try: “We had an ongoing problem where our team was over-ordering energy supplies by a wide margin every month, which had real cost implications. I built a forecasting model that gave us a more accurate week-ahead forecast, which directly reduced our overage costs.”
2. Do Research
I suggest starting with a basic Google search: “[Company Name] ethical discussion questions”. You can find information on Glassdoor, Reddit, and other smaller websites. At large companies especially, you'll often find threads where past people share the actual questions they were asked, what the format looked like, and how the process felt. Remember that groups change their questions over time, so don't take old reviews as gospel, but they will still give you a solid idea of what the company values and how they like to interview you.
You can too See a list of common ethical interview questions for your specific role (Data scientist, data engineer, data analyst). A data scientist may be asked more about abstract projects and model trade-offs. The data analyst may be faced with many questions about the communication obtained from the leads.
Search for YouTube videos of funny behavioral interviews or people who have done multiple rounds of data science interviews. Seeing how someone else responds will teach you more than reading a list of tips. Pay attention:
- What situations has the candidate been exposed to, and what are similar ones you have been in
- The candidate's facial expression and overall demeanor
3. Prepare a Few Situations Ahead of Time
Most advice for preparing for a behavioral interview focuses on conflict: “Tell me about a time you disagreed with your colleague” or “Describe a situation where you failed.” Those questions are important, but in data science roles, it's a difficult category to understand.
- “Tell me about a time you had to make a decision without having all the information you needed.”
- “Describe a project where requirements changed over time.”
- “How do you handle situations where the data doesn't support a clear answer?”
These questions are specifically designed to test something very important in data science: your tolerance for uncertainty and your ability to move forward without complete information.
The best way to plan for this is to use the STAR method.
STAR stands for:
- The situation: What was the context/background?
- Job: What task were you specifically given to do/solve?
- Action: What steps did you take to solve the problem?
- The result: What was the result?
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.”
Condition: In the middle of a forecasting project, I discovered that two months of energy usage history data had been entered incorrectly due to a meter error within the training window I planned to use.
Job: My stakeholders need a working model delivered at the end of the sprint. I had to decide whether to delay the project to further investigate the data issue, or continue with the modified method and flag the risk.
Action: I adjusted the affected window from the training set, retrained on clean data, and did a quick analysis to determine how much predictive power I might have lost. I presented both options to my participants (delay with more certainty, or deliver on time with written predictions) and let them call with full information.
Result: We were able to implement the model on time. We achieved a 12% reduction in absolute error compared to the existing baseline, and our forecasts for the coming week were accurate enough to reduce capacity over-ordering by ~18% in the first month of implementation. A participant later told me that being transparent about a data problem actually increased their confidence in the results, not the other way around.
Take the time to write 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've planned, having a few scenarios at hand to draw from is still better than having a blank mind at the moment.
Conclusion + Bonus Tip
In my first year as a data scientist, I learned that work is rarely about getting the perfect answer. It's about finding one that's secure, fast enough to be useful. Participants are not waiting for complete information. Business decisions have deadlines. The ability to say “here is the supporting data right now, and here are the ideas I made” is a skill in itself.
So before your interview, think about times when:
- Submit a recommendation before the model is complete
- It has been pointed out that the project has been relocated and replaced
- He made the judgment call and owned the results
- He spoke about the uncertainty clearly rather than hiding it
Then write down a few of these situations before your interview. That way, they will be fresh in your mind.
Here's a final bonus tip: Remember to smile, keep it simple, and have a positive attitude. This can make a much bigger difference in your conversation than you think. Try to make small talk with the interviewers. Find something in common with them. Don't be afraid to make a little joke. You'd be surprised how far this can take you and make you stand out from other candidates.
