Data · Role guide

Data Scientist Interview Questions & Preparation

Quick answer

A data scientist interview covers statistics and probability, a coding round (SQL and Python), a machine-learning or case round, and behavioural questions. Expect to reason about experiments, metrics and model trade-offs, and to explain technical work clearly to non-technical stakeholders.

Overview

Data science interviews test whether you can turn ambiguous business problems into measurable analyses. Strong candidates pair technical depth (stats, ML, SQL) with clear communication and product judgement.

The Data Scientist interview process

Data Scientist interview process1Recruiter screen2Technical screen3ML / case round4Behavioural & stakeholder
Data Scientist interview process
StageWhat to expect
Recruiter screenBackground and a quick stats or SQL warm-up.
Technical screenSQL and Python coding, plus probability and statistics questions.
ML / case roundDesign an experiment or model for a business problem and discuss metrics.
Behavioural & stakeholderCommunicating insights and influencing decisions with data.

Data Scientist interview questions

These are the questions you're most likely to be asked. Prepare a structured answer for each, and a STAR story for the behavioural ones.

  1. 1Explain p-values to a non-technical stakeholder.
  2. 2Write a SQL query to find the second-highest salary per department.
  3. 3How would you design an A/B test for a new feature?
  4. 4When would you use precision vs recall?
  5. 5Tell me about a time your analysis changed a business decision.
  6. 6How do you handle missing data?
  7. 7Explain the bias–variance trade-off.
  8. 8How would you detect and handle outliers?

Example STAR answer

The STAR method: Situation, Task, Action, ResultSSituationSet the sceneTTaskYour responsibilityAActionWhat you didRResultThe measurable outcome
The STAR method — structure every behavioural answer around these four steps.

Example answer to: Tell me about a time your analysis changed a business decision.

SituationThe growth team wanted to spend more on a channel that looked high-performing.
TaskI was asked to validate the channel's true incremental value.
ActionI ran a geo-based holdout test, controlled for seasonality, and isolated incremental conversions.
ResultThe channel's real lift was a third of the reported figure; we reallocated £180k to higher-incrementality channels.

Skills assessed

Statistics & probabilitySQLPythonExperimentation (A/B testing)Communication

How to prepare for a Data Scientist interview

  • Define the metric and success criteria before reaching for a model.
  • Practise SQL window functions and probability puzzles.
  • Explain technical concepts in plain language — it's tested directly.
  • Tie every analysis to a business decision.

Common mistakes to avoid

  • Over-engineering a model when a simple analysis answers the question.
  • Forgetting to define how you'd measure success.
  • Jargon-heavy answers that lose a non-technical audience.
  • Ignoring data quality and experiment design.

Data Scientist salary

UK data scientists typically earn £45k–£85k, with senior and specialised roles exceeding £100k.

Frequently asked questions

Is SQL tested in data science interviews?

Yes — SQL is almost always tested, including joins, aggregations and window functions.

How much machine learning do I need?

Enough to choose appropriate models, explain trade-offs and evaluate them properly. Depth scales with the role's seniority and specialism.

What stats topics come up most?

Hypothesis testing, p-values, confidence intervals, A/B testing, and the bias–variance trade-off.

Preparing for a Data Scientist interview?

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