AI data scientist interview
Three questions about the judgment behind a model: imbalanced classes, an unfamiliar dataset and metrics beyond accuracy. Because the demo is conversational, you can take it on a phone call or in a browser.
Free to take with a work email. No account needed.
The 3 questions the AI interviewer asks
Word for word from the demo. In the demo and in a real interview, the AI also asks its own follow-ups from what the candidate says.
- 1
“Describe a time you had to deal with imbalanced data in a classification problem. What techniques did you consider, and which did you apply?”
What a strong answer shows
- Weighs options: class weights, resampling, threshold tuning or more minority examples
- Resamples inside training folds only, so the test data stays untouched
- Ties the choice to the costs of false positives and false negatives
- 2
“Imagine you're given a new dataset for a predictive task, but you have no context about its features. How would you begin to explore and understand this data?”
What a strong answer shows
- Profiles first: types, missing values, distributions, cardinality and duplicates
- Asks for a data dictionary or a domain expert before guessing meanings
- Checks the target for leakage and too-good-to-be-true correlations
- 3
“Beyond accuracy, what other metrics do you consider important when evaluating a machine learning model, and why?”
What a strong answer shows
- Matches the metric to the decision: precision, recall, F1 or PR AUC
- Checks calibration when predicted probabilities drive real decisions
- Breaks results down by segment and over time, not one overall number
What this demo shows
This demo is a spoken interview, so it works two ways: on video in a browser, where the demo interviewer can see and hear you, or on a phone call. The AI generates its own follow-ups from what you say, so naming a technique can lead straight to a question about why you chose it. In a full AI interview for data scientists, the employer's team also writes follow-ups for each question, and a browser interview can include work in the shared code editor, in Python or SQL.
The demo and a real interview
- Aspect
- Demo
- Real interview
- Questions
- 3 sample questions
- Your own, with follow-ups your team writes plus the AI's own
- Length
- About 5 minutes
- 30 to 45 minutes is normal
- Scoring
- A scored report in your inbox
- Your scorecard and grading guides, with a scoring trace
Common questions
Can data scientists write code during the interview?
Yes, in a browser interview. The shared code editor supports Python and SQL among other languages, and the AI interviewer sees the code as it is typed and asks about it. This demo is conversational, which is why it can also be taken by phone; coding questions need the browser.
Will the AI ask follow-up questions?
Yes, of two kinds. Your team writes follow-ups for each question, and the AI generates its own from the candidate's answer. It can also probe for a particular skill using that skill's grading guide, so a vague answer does not have to be the last word.
Can it test how candidates work with AI tools?
That is what Practical AI Fluency is for. It is new and rolling out: in-interview AI exercises and hands-on labs that measure how well a candidate works with AI tools. The team can tell you whether it is available for your account yet.
Interview your own candidates this way
Your questions, your scorecard, the same guide for every candidate.