Data Scientist interview questions

Tell me about a time you disagreed with a teammate about an analytical approach. How did you reach a decision?

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Tell me about a time evidence caused you to change your initial approach.

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Which metric would you choose for a new product feature, and what failure modes would you monitor?

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Tell me about a time you changed a decision by communicating a complex analysis clearly.

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Tell me about a time you discovered that your analysis was wrong. What did you do next?

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Tell me about a time you explained a technical result to a non-technical partner.

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Tell me about an analysis that changed a decision and how you explained it to a non-technical partner.

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Tell me about a time your analysis changed a team decision.

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Tell me about a time you changed your analysis after receiving contradictory evidence.

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Tell me about a time new evidence caused you to change your analytical approach.

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Tell me about a real situation where your analysis changed a decision.

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Tell me about a time you took ownership of an ambiguous analysis.

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Tell me about a disagreement over an analysis and how you worked with your collaborator to resolve it.

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Why do you want to join Boston Consulting Group as a Data Scientist?

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Tell me about a project where your analysis changed a decision.

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Can you describe a time you changed your analysis after receiving new evidence?

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Tell me about a model whose performance did not translate into a useful business decision.

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Tell me about a time when new evidence caused you to revise an analytical recommendation.

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How would you validate an equipment failure model when failures are rare and observations from the same device are correlated?

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How did you handle a project where a stakeholder disagreed with your interpretation of the data?

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Can you describe a time you changed your recommendation after discovering a flaw in your analysis?

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Tell me about a time your analysis challenged a team's preferred conclusion. How did you handle the disagreement?

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How did you handle a disagreement with a stakeholder about what an analysis showed?

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Write a SQL query that compares engagement across customer groups and explain how you would validate the result.

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How would you write a SQL query to compare retention across customer cohorts while avoiding duplicate records?

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Write a SQL query that compares engagement across user groups while handling missing activity.

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How would you write a SQL query to identify repeated user activity and verify that duplicates do not distort the result?

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Write a SQL query that compares cohort retention while handling missing records.

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Given an events table and a users table, how would you write SQL to compare conversion across cohorts?

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How would you write a SQL query to compare retention across cohorts while handling missing events?

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How would you write a SQL query to identify users whose activity declined across consecutive periods?

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How would you write a SQL query to compare retention across experiment groups while handling late-arriving events?

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Write SQL to find members whose activity increased from one period to the next.

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Write a SQL query to compare conversion rates across customer segments while handling missing values.

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How would you write a SQL query to compare retention across customer segments?

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How would you write a SQL query to compare retention across customer groups?

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How would you write a SQL query to compare retention across merchant cohorts?

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How would you structure code to clean, validate, and analyze an unfamiliar dataset?

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What SQL query would you write to identify a sustained change in listening behavior?

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Write a SQL query to compare engagement across cohorts while avoiding duplicate events.

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Write a SQL query to compare retention across cohorts and explain how you would validate the result.

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Write a SQL query to identify users whose activity declined across consecutive periods.

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How would you write a query to identify changes in customer behavior over time?

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How would you write a SQL query to compare conversion rates across experimental groups while checking for missing data?

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Given an event table and a customer table, how would you write a query to compare retention across cohorts?

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How would you write a SQL query to identify unusual changes in transaction activity?

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An events table contains repeated records and timestamps. How would you write SQL to identify users who returned after their first active day without double-counting them?

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How would you write a SQL query to find the latest valid reading for each device while handling duplicate timestamps?

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How would you write SQL to calculate payment success by merchant using payment identifiers, attempt identifiers, timestamps and outcomes?

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How would you design an experiment to measure whether a product change improves user retention?

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How would you design an experiment to test whether a recommendation change improves customer outcomes?

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How would you design an experiment to measure whether a product change improves user retention?

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How would you design an experiment when random assignment is constrained?

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How would you design an experiment to measure whether a new product feature improves user retention?

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How would you design an experiment to determine whether a product change improves user outcomes?

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How would you design an experiment to measure whether a product change improves rider retention?

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How would you design an experiment to test whether a recommendation change improves member engagement?

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How would you design an experiment to measure whether a recommendation change improves meaningful engagement?

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How would you design an experiment to determine whether a product change improves customer retention?

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How would you design an experiment to measure whether a product change improves user engagement?

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How would you design an experiment to determine whether a product change caused an improvement?

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How would you design an experiment to test a change in merchant checkout behavior?

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How would you design an experiment to test a change in listener engagement?

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How would you design an experiment to test whether a product change improves creator retention?

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How would you design an experiment to decide whether a product change improved user outcomes?

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How would you design an experiment to test whether a product change improves customer retention?

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How would you design an experiment when random assignment is not possible?

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How would you design an experiment to test whether a product change improves customer retention?

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How would you design an experiment to measure whether a new customer feature changes engagement?

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How would you design an experiment to determine whether a product change affected customer behavior?

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How would you design an experiment to test whether a manufacturing process change improves yield?

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How would you design an experiment when changes for guests could also affect hosts?

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How would you design an experiment to test a new onboarding flow?

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A key product metric declined after a launch. How would you investigate the cause?

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How would you choose between an interpretable model and a more complex model with better predictive performance?

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How would you explain an uncertain model result to a non-technical leader who needs to act on it?

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How would you investigate conflicting movement between an engagement metric and a guardrail metric?

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How would you choose and validate a model for forecasting demand when the data changes over time?

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How would you explain a model result to an operations leader who does not work with statistics?

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How would you determine whether a product change caused an observed lift in retention?

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How would you detect and address selection bias in an observational analysis?

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How would you explain a statistically significant but practically small result to a product leader?

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A key metric increased after launch. How would you determine whether the change caused the improvement?

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How would you detect and address selection bias in a training dataset?

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How would you compare two modeling approaches when predictive performance and interpretability point in different directions?

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How would you explain a model recommendation and its uncertainty to a non-technical stakeholder?

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How would you decide whether a metric change is statistically meaningful and practically useful?

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How would you choose between an interpretable baseline and a more complex model?

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A model performs well offline but poorly after deployment. How would you investigate?

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How would you explain a counterintuitive result to a non-technical audience and recommend a next step?

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How would you investigate a metric movement that appears in only one user segment?

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When would you prefer a simpler model to a more accurate but less interpretable one?

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How would you explain an inconclusive experiment to a product leader?

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How would you investigate an experiment whose primary metric improved while a guardrail metric worsened?

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How would you choose between an interpretable baseline and a more complex model?

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How would you explain a small, uncertain effect to a non-technical decision-maker who wants a clear recommendation?

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What is an example of a time when new evidence caused you to change an analytical recommendation?

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What checks would you perform before trusting the result of a randomized experiment?

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How would you choose between a simple interpretable model and a more complex model?

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An experiment improves conversion but increases cancellations. How would you investigate and communicate the tradeoff?

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What biases could invalidate an experiment result, and how would you detect them?

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How would you choose between a simple model and a more complex model for predicting viewing behavior?

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A metric improves overall but declines for a meaningful segment. How would you investigate and communicate the tradeoff?

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A treatment raises clicks but reduces downstream retention. How would you interpret the result?

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How would you choose between an interpretable model and a more accurate opaque model?

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How would you explain a confidence interval to a product partner without a statistics background?

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How would you choose between an interpretable model and a more complex model for a high-stakes prediction?

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Your model performs well offline but poorly after deployment. How would you investigate?

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Explain a confidence interval to a business stakeholder who does not work with statistics.

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What checks would you run before trusting the result of an experiment?

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How would you investigate a sudden change in a key product metric?

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How would you choose between a simple baseline and a more complex predictive model?

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How would you explain an inconclusive analysis to a non-technical stakeholder?

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What checks would you run before trusting an experiment result?

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How would you choose between a simple model and a more complex model?

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How would you diagnose a model that performs well in validation but poorly after deployment?

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How would you explain an uncertain result to a non-technical stakeholder?

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Which assumptions would you check before interpreting an experiment result?

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How would you choose between a simple model and a more complex model for a business decision?

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A key metric changed unexpectedly. How would you investigate the cause?

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How would you explain a statistically uncertain result to a non-technical partner?

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How would you choose between a simpler model and a more accurate but less interpretable model?

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How would you explain an inconclusive experiment to a non-technical stakeholder?

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Describe how you adapted when new evidence changed your recommendation.

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A key metric rises after launch, but a guardrail metric falls. How would you investigate and recommend a next step?

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How would you choose between an interpretable model and a more accurate but less transparent model?

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How would you explain a confidence interval and its decision value to a product manager?

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How would you choose between an interpretable baseline and a more complex model?

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A key metric moved unexpectedly. How would you diagnose the change?

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How would you explain your analysis to a non-technical decision-maker who disagrees with your recommendation?

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Describe an open-ended data problem, the alternatives you considered, and why you chose your final approach.

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How would you detect and address selection bias in an observational dataset?

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Which model would you choose for a rare-event prediction problem, and how would you validate it?

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How would you explain a model's false-positive tradeoff to a non-technical executive?

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Describe a difficult project, the choices you made, and what you learned.

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How would you choose between an interpretable model and a more accurate complex model?

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What checks would you run before trusting a model's performance?

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Explain a statistical result to a stakeholder without a technical background.

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Describe a disagreement about an analytical approach and how you handled it.

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What checks would you run before trusting a predictive model in production?

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How would you choose between a simpler model and a more accurate but less interpretable model?

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A client wants to reduce customer churn. How would you structure the problem and decide which data to request?

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How would you explain a confidence interval and its business meaning to a non-technical stakeholder?

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How would you detect data leakage before training a predictive model?

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Which metric would you choose for an imbalanced classification problem, and why?

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How would you explain a confidence interval to a stakeholder without a statistics background?

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What checks would you perform before training a model on time-dependent data?

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How would you choose between a more accurate model and a more interpretable one?

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How would you explain a model's limitations to a non-technical risk partner?

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An experiment increases engagement but lowers retention. How would you decide whether to recommend launching the change?

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How would you distinguish an effect caused by a product change from seasonality or a shift in the users being measured?

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A model performs well during validation but poorly after deployment. What would you investigate before deciding whether to replace it?

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How would you explain an uncertain result to a nontechnical stakeholder who needs to make a launch decision?

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Why does this Data Scientist role interest you, and which project best demonstrates the work you want to do?

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How would you choose a randomization unit and success metric for an experiment on an equipment alerting feature?

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What would make you choose a simple baseline over a more accurate but harder to explain model?

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How would you explain a statistically significant result that is too small to change an operational decision?

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How would you test a checkout change when merchants differ in size and customers can make repeated payment attempts?

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What could explain an apparent increase in payment success that disappears when merchants are grouped by size?

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How would you evaluate a fraud model when labels arrive late and incorrectly declining a legitimate payment has a business cost?

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How would you explain an inconclusive experiment to a stakeholder who needs to make a launch decision?

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What could make a join between equipment readings and production records inflate your results, and how would you detect it?

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How would you evaluate a model that flags rare manufacturing defects?

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What would you investigate if a model performed well during validation but poorly on a later production batch?

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How would you explain an uncertain result to a stakeholder who needs to make a decision?

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How would you define success for a change to search ranking in a booking marketplace?

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Given users, bookings and cancellations tables, how would you calculate completed bookings per active user without double counting?

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What would you investigate if an experiment improved the primary metric but increased cancellations?

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How would you validate a model that predicts booking cancellations while avoiding information leakage?

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How would you explain an inconclusive experiment to a product manager deciding whether to launch?

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What does a p value actually mean, and what does it not mean?

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Explain a confidence interval to a product manager in two sentences.

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When would you use a t test instead of a z test?

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What is the central limit theorem and why does it matter for your work?

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A metric moved and the result is significant. Why might it still be wrong?

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Explain Type I and Type II error using a decision your team actually makes.

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What is selection bias and how would you detect it in your data?

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When is a median a better summary than a mean?

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How do you choose sample size, and what happens if you cannot reach it?

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Your test is significant after three days. Do you ship it?

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What is a novelty effect, and how would you separate it from a real lift?

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How do you handle multiple metrics moving in different directions?

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What would you do if randomisation was broken partway through the test?

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How would you measure something you cannot randomise, like a pricing change across a whole market?

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How would you build a model to predict customer churn? Start with how you would define churn.

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Your model has 95 percent accuracy on an imbalanced dataset. Is it good?

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Explain the bias variance tradeoff using a model you have shipped.

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How would you decide between a simple model and a complex one for this problem?

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How do you know a model has stopped working after deployment?

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Explain regularisation to someone who has not used it.

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How would you find and handle duplicate rows in a transactions table?

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How would you measure whether a new feature is successful?

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Which single metric would you put on the leadership dashboard, and what would you leave off?

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How would you tell a good retention curve from a bad one?

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