- Stages
- 5
- Typical timeline
- 3 to 6 weeks
- Difficulty
- Hard
- Format
- Technical screen, take-home or case, then a loop
Data scientist interviews usually cover SQL, statistics and experimentation, machine learning concepts and a product or business case. Product-focused roles weigh experiment design and metrics heavily, while modelling roles go deeper into machine learning. Expect to design an A/B test, interpret ambiguous results, choose metrics for a feature and explain a model to a non-technical audience. Take-home assignments are common. Interviewers reward candidates who start simple, check assumptions, think about bias in the data and always link results to a decision.
The interview process, stage by stage
- Recruiter screenBackground, projects and type of data science role.
- Technical screenSQL, Python and statistics questions.
- Take-home or caseAnalyse data or design an experiment and present it.
- Product sense and metricsDefine metrics and design tests for a product change.
- Final loopModelling discussion, behavioural and stakeholder rounds.
How you're scored
Sound experiment design and correct interpretation.
Clean SQL and Python, appropriate models.
Chooses the right metrics and connects work to decisions.
Explains complex results simply.
Likely Data Scientist interview questions
Design an A/B test for a new checkout button.
A strong answer includes: Hypothesis, primary metric such as checkout conversion, guardrails, user-level randomisation, sample size and duration, and how you would decide from the result.
Treatment users have higher retention, but they also chose to opt in. Can you trust the result?
A strong answer includes: No, because of selection bias. Explain confounding and suggest randomisation, matching or other causal methods to estimate the true effect.
How would you predict which customers will churn?
A strong answer includes: Define churn and the prediction window, build features from behaviour, start with a simple model, evaluate with suitable metrics and explain how predictions would drive action.
Every question for this interview, with what strong answers include. Free with an account for the first few; all of them with Pro.
Common mistakes
- Over-complicated models without a baseline
- Ignoring selection bias and confounders
- Stopping tests early when results look good
- No clear decision at the end of an analysis
Questions to ask them
- Is this role more focused on experimentation or modelling?
- How are experiment results reviewed and acted on?
- What data infrastructure is available?
- Which teams does data science work with most?
Candidate experiences
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Share your interviewFrequently asked questions
What is asked in a data scientist interview?
SQL and Python, probability and statistics, A/B test design and interpretation, product metrics questions, machine learning concepts and often a take-home analysis. Behavioural questions focus on influencing decisions with data.
How do I design an A/B test in an interview?
State the hypothesis and primary metric, add guardrail metrics, choose the unit of randomisation, estimate sample size from the minimum effect you care about, set duration, and plan for pitfalls such as novelty effects and network effects.
How much machine learning is in data scientist interviews?
It depends on the role. Product data science roles focus on experiments and metrics with basic ML questions. Modelling roles test algorithms, feature engineering, evaluation and deployment in more depth.
How do I approach a data science take-home?
Clarify the question, explore and clean the data, start with a simple approach, check results, and present a clear recommendation with limitations. Readable code and a concise write-up matter more than a complex model.
