Data scientist mock interviews with an AI interviewer
Data science interviews test three things at once: whether your statistics are sound, whether you can build and evaluate a model, and whether you can explain either to someone who has never heard of a p-value.
PrepLinx runs each round out loud, so you practice the part that's hardest to rehearse alone: explaining your reasoning while someone asks why. A code editor and whiteboard are open the whole session for working through a calculation, sketching a pipeline or writing a quick script.
4 data scientist rounds to practice
Each round has its own AI interviewer, problem and scoring. Practice them one at a time, at the difficulty you choose.
Behavioral
Times your analysis changed a decision, how you handled messy data or pushback, and how you explain results to stakeholders.
Up to 30 minutesTechnical
A broad technical discussion: statistics, machine learning, working with large datasets, preprocessing and model evaluation.
Up to 30 minutesML/Modeling
Take a business problem to a model: framing, features, algorithm choice, validation and tuning.
Up to 60 minutesStatistical
Hypothesis testing, probability, A/B test design, confidence intervals and regression, with an emphasis on interpreting results correctly.
Up to 30 minutesWhat interviewers look for
Statistical reasoning
Choosing the right test, stating its assumptions and interpreting the result without overclaiming.
Modeling judgment
Picking an approach that fits the data and the business goal, and validating it honestly.
Communication
Explaining a technical result in plain language and saying what it means for the decision.
Business sense
Starting from the question the business needs answered, not from the technique you'd like to use.
Practice questions
Examples of the questions and follow-ups you can expect. Practice saying your answers out loud, not just reading them.
- How would you design an A/B test for a new checkout button, and how long would you run it?
- Explain a p-value to a product manager without using jargon.
- Your fraud model is 98% accurate. Why might that number be misleading?
- A feature is missing for 40% of rows. What do you do?
- Walk me through building a churn prediction model from scratch.
- Tell me about a time your analysis changed a decision.
How a PrepLinx session works
- 1
Choose the round
Pick the role, the interview type and a difficulty (easy, medium or hard). Optionally add a job profile with your resume and the job description, so the questions relate to that job.
- 2
Interview out loud
The AI interviewer speaks, listens to your answers and asks follow-ups. It doesn't give hints or say whether you're right, just like the real thing.
- 3
Get scored feedback
A report with a 0–10 score for each dimension the round is graded on, strengths quoted from your own words, weaknesses, concrete improvements and an overall hiring signal, plus the full transcript.
How to prepare
Start from the decision
Before choosing a test or a model, say what decision the analysis will support. Interviewers listen for it.
Say your assumptions out loud
Independence, sample size, stationarity: naming them, and how you'd check them, matters as much as the calculation itself.
Know your metrics cold
Be ready to explain precision, recall, ROC AUC and calibration, and when each one is the wrong choice.
Practice the plain-language version
For every technical concept you rely on, have a one-sentence explanation a non-technical stakeholder would follow.
Frequently asked questions
Which data science rounds can I practice?
Statistics, ML modeling, technical discussion and behavioral. You set the difficulty (easy, medium or hard) for each session.
Is there SQL practice for data science interviews?
SQL isn't a separate round in the data science track. If your loop has a SQL screen, the SQL/ETL and SQL + Python rounds in the data engineer track are the closest practice, and any account can choose them.
Can I write code during the interview?
Yes. Every session has a code editor with 17 languages, including Python, R and SQL, and you can run your code. The interviewer sees it and asks about it.
How is my performance scored?
Every session ends with a report: a 0–10 score for each dimension of the round and for your communication, strengths quoted from your own answers, weaknesses, concrete improvements and an overall hiring signal.
Other roles
Software engineer
Practice software engineer interviews out loud with an AI interviewer: coding, system design, AI-assisted coding and behavioral rounds, with scored feedback.
Product manager
Practice product manager interviews out loud with an AI interviewer: product sense, product strategy, technical and behavioral rounds, with scored feedback.
Machine learning engineer
Practice machine learning engineer interviews with an AI interviewer: ML system design, ML coding, technical and behavioral rounds, with scored feedback.
Data engineer
Practice data engineer interviews with an AI interviewer: SQL, ETL, data modeling, pipeline design, Python and behavioral rounds, with scored feedback.
Practice your next data scientist interview
4 full interviews free, with scored feedback. No credit card, and they don't expire.
Start practicing free