Intern - IT Data Science (Summer 2027)
Posted Sep 1, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a data project you're proud of. What question were you answering and what did you find?
What they are checking: Whether they start from a real business question rather than from a model they wanted to try.
Tell me about a time you had to present findings to people who didn't have a technical background.
What they are checking: Whether they can turn numbers into a clear story with a recommendation, which decides whether their work gets used.
Tell me about a time you got stuck on a technical problem. How long did you wait before asking for help?
What they are checking: Whether they try hard on their own but know when asking saves everyone time, a key intern habit.
What would you do
Your model looks great on test data, then performs poorly in real use. What would you look into?
What they are checking: Whether they know about overfitting, data leakage and drift, and can debug calmly instead of blaming the data.
A manager asks for a dashboard by Friday, but you think the real problem needs a different kind of analysis. What do you do?
What they are checking: Whether they can respectfully push back with reasons while still delivering something useful on time.
How would you handle data that includes customer or employee information you weren't expecting to see?
What they are checking: Whether they respect privacy and know to stop and ask rather than quietly using sensitive data.
About the work
A manufacturing line produces machine data every second. What would you check before trusting that data for analysis?
What they are checking: Whether they think about missing values, timestamps, sensor errors and duplicates before building anything on top of the data.
How would you explain the difference between a classification model and a regression model to someone on the shop floor?
What they are checking: Whether they truly understand the basics well enough to explain them in plain language to non-technical partners.
Write out, out loud, how you'd use SQL to find the top five products with the highest defect rate last month.
What they are checking: Whether they can join, group and filter correctly and think about how a rate is calculated rather than raw counts.
Which tools and languages are you most comfortable with, and which would you want to get better at this summer?
What they are checking: Whether they give an honest picture of their skills and show a plan to grow instead of overstating ability.
Plexus builds products like surgical systems and diagnostic instruments. Why might data accuracy matter more here than at a retail company?
What they are checking: Whether they connect data quality to product safety and regulated customers rather than treating all analysis the same.
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Practice this out loudThe full job description
As published by Plexus.