Skip to main content
riwork

Data & AI

Data Scientist mock interview

Uses statistics, experiments and predictive models to answer business questions and guide product decisions. Day to day that means pulling and cleaning data, running analyses or tests, building models, and explaining results to decision makers. A good interview for this role tests whether you can show that work, not just name it.

Ava, the AI interviewer, will ask you questions on statistics & experimentation, predictive modelling, data wrangling & sql and model evaluation and 4 more skills, follow up on what you say, and finish with a report that quotes your own words back to you.

Start a data scientist practice interview

Skills assessed in a data scientist interview

  • Statistics & experimentation

    Role skill

    Explains significance, power and effect size plainly and designs tests with sensible metrics, sample sizes and guardrails.

  • Predictive modelling

    Role skill

    Chooses models for the problem and constraints, explains overfitting and regularisation, and justifies complexity with measurable gains.

  • Data wrangling & SQL

    Role skill

    Describes concrete steps to profile, clean and join messy data, and validates that fields mean what they appear to mean.

  • Model evaluation

    Role skill

    Picks metrics that match business cost, handles imbalance and leakage, and plans for monitoring after launch.

  • Causal reasoning

    Role skill

    Separates correlation from causation and names suitable methods like difference in differences or matching along with their assumptions.

  • Problem framing

    Role skill

    Starts from the decision to be made, clarifies success criteria and constraints, and questions whether modelling is needed at all.

  • Communicating findings

    Behavioural

    Leads with the recommendation, translates uncertainty into plain language, and stays honest when results are unwelcome.

  • Collaboration & influence

    Behavioural

    Builds agreement with product and engineering through shared evidence, listening to objections and adjusting the plan.

Sample data scientist interview questions

8 of the 12 questions Ava can ask for this role, one or more per skill. In a real session she adapts them to your level and follows up on your answers.

  1. Q1Statistics & experimentationEntry level

    How would you explain a p-value to a product manager who has never taken a statistics course?

    A strong answer: The chance of seeing a result this extreme if there were no real effect, and what it does not tell you, like the size of the effect.

  2. Q2Data wrangling & SQLEntry level

    You get a dataset where about a fifth of the values in an important column are missing. How do you decide what to do?

    A strong answer: Investigates why data is missing and whether it is random, then compares dropping, imputing or flagging and checks the impact on results.

  3. Q3Predictive modellingEntry level

    What is overfitting, and how would you spot it in a model you just trained?

    A strong answer: Model learns noise instead of patterns, detected by a gap between training and validation scores, fixed with simpler models, regularisation or more data.

  4. Q4Model evaluationMid level

    You're building a fraud model where only one in a thousand transactions is fraud. How would you evaluate whether it's any good?

    A strong answer: Rejects accuracy, uses precision, recall and precision recall curves, sets thresholds from the cost of misses versus false alarms.

  5. Q5Problem framingMid level

    A stakeholder asks you to build a churn model. What questions do you ask before writing any code?

    A strong answer: What decision or action the prediction drives, how churn is defined, time horizon, data available and how success will be measured.

  6. Q6Communicating findingsMid level

    Tell me about a time your analysis showed something leadership didn't want to hear. How did you present it?

    A strong answer: Clear headline, solid evidence and caveats, anticipates objections, and offers a constructive next step rather than just bad news.

  7. Q7Causal reasoningSenior

    When you can't run an experiment, how do you estimate whether a new feature actually caused an increase in retention?

    A strong answer: Quasi experimental methods like difference in differences, synthetic control or matching, with stated assumptions and sensitivity checks.

  8. Q8Collaboration & influenceSenior

    Tell me about a time you had to get engineering and product teams to change direction based on data. How did you build agreement?

    A strong answer: Involves people early, frames evidence around their goals, listens to objections, and accepts or adapts when challenged fairly.

What a strong data scientist answer looks like

  • Statistics & experimentation

    Explains significance, power and effect size plainly and designs tests with sensible metrics, sample sizes and guardrails.

  • Predictive modelling

    Chooses models for the problem and constraints, explains overfitting and regularisation, and justifies complexity with measurable gains.

  • Communicating findings

    Leads with the recommendation, translates uncertainty into plain language, and stays honest when results are unwelcome.

How the practice works

  1. Step 1

    Choose your level

    Pick intern through lead. The data scientist question bank is labelled for 3 entry-level, 4 mid-level and 3 senior questions (plus 2 for any level), and Ava leans toward the ones that fit.

  2. Step 2

    Answer out loud or by typing

    Ava asks 4 or 7 main questions, depending on the length you choose, such as the statistics & experimentation question above. You can also add a short coding exercise. Typing is always available if you can't speak.

  3. Step 3

    Get feedback you can act on

    Each skill is scored against the evidence you gave, with the specific points from your answers. In practice mode you also get a check against the situation, task, action and result structure, a stronger version of each answer built only from what you said, and what to practise next.

Try the data scientist interview

Frequently asked questions

Which skills does the Data Scientist mock interview cover?
Statistics & experimentation, Predictive modelling, Data wrangling & SQL, Model evaluation, Causal reasoning, Problem framing, Communicating findings and Collaboration & influence. The first 6 are role skills; communicating findings and collaboration & influence are the behavioural part.
What kind of questions will I get?
Questions come from a bank of 12 written for this role. An entry-level one is “How would you explain a p-value to a product manager who has never taken a statistics course?” and a senior one is “When you can't run an experiment, how do you estimate whether a new feature actually caused an increase in retention?”.
How will my data wrangling & sql answer be judged?
Against what strong evidence looks like for that skill: describes concrete steps to profile, clean and join messy data, and validates that fields mean what they appear to mean. Anything you don't mention is not credited, and nothing is invented for you.
Is this useful for DS or applied scientist roles?
Yes. DS, applied scientist, decision scientist and machine learning scientist are treated as close to data scientist, so the same skills and questions apply. You can also type any other role on the setup screen.
Is it free, and do I need an account?
Starting practice is free, and a free account is required before the interview begins. The feedback is guidance for your preparation, not a prediction of any hiring decision.

Practise related roles