Data Science Intern - Summer 2027
Posted Sep 16, 2026 · We last checked this listing on Sep 20, 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.
Behavioral
Tell me about a project, class or otherwise, where you used statistics or machine learning to answer a real question. What was the question and what did you find?
whether the candidate can apply data science concepts outside a textbook, not just recite theory
Tell me about a time you had to work with a team or stakeholders who had a different background than you, like engineers or manufacturing staff. How did you make sure you understood what they needed?
cross-functional collaboration skill, given the role sits inside a product development environment
Tell me about a time a project deadline was tight and your first analysis approach wasn't working. What did you do?
adaptability and time management under real deadlines, relevant to a fast-paced summer internship
What's something you learned recently on your own, outside of class, related to data science, AI, or engineering?
curiosity and self-directed learning, important for an intern expected to ramp up quickly
Technical
Walk me through how you would clean and prepare a messy dataset before building a model. What do you check for first?
practical data-wrangling habits and awareness of data quality issues
What tools or languages have you used for analysis, like Python, R, or SQL, and what's a project where you leaned on one of them the most?
actual hands-on toolset versus a resume list
Explain a statistical or machine learning concept, like p-values or overfitting, the way you'd explain it to an engineer who doesn't do data science day to day.
communication skill and depth of understanding, since the posting stresses writing clearly for a technical but non-DS audience
Have you worked with CAD systems like Pro-E or AutoCAD, or with bills of materials? What did you use them for?
exposure to the engineering data structures this role's data will actually come from
Situational
Say you're given sensor data from a generator that's running in the field, along with test bench data from the lab. How would you think about combining or comparing those two sources?
comfort connecting product and operational data, since the role touches connected-product analytics
If you built a model that predicted product failures but it disagreed with what the reliability engineers believed from experience, what would you do?
how the candidate handles pushback and balances data-driven conclusions with domain expertise
How would you go about figuring out which factors are driving warranty claims or field failures on a product line, if you had access to manufacturing, test, and customer data?
ability to structure an open-ended analytics problem using multiple data sources
This internship is full-time on-site through the summer, with the chance of continuing part-time during the school year. How are you thinking about balancing that with your coursework or other commitments?
realistic understanding of the commitment and whether the schedule actually fits the candidate's life
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As published by Generac.
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