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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
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Showing 1–30 of 641 results
- AdobeEasyData ScientistDescribe Your Background and Alignment with This Role
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Background and Alignment with This Role states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AdobeMediumData ScientistDesign Metrics Framework for Adobe Express Performance Evaluation
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics Framework for Adobe Express Performance Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - AdobeMediumData ScientistSolve SQL and Python Data Manipulation Tasks
This question evaluates proficiency in SQL event-log aggregation and basic Python data-structure transformations, assessing competencies such as counting distinct users, identifying frequent event types, and performing dictionary/list/string manipulations.
Data Manipulation (SQL/Python)Onsite - AdobeEasyData ScientistUnderstand P-Value's Role in Product Decision-Making
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Understand P-Value's Role in Product Decision-Making states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - AirbnbHardData ScientistDesign and assess an A/B test
This problem tests your ability to design a statistically sound A/B test for a marketplace platform, including metric selection, power analysis considerations, and handling potential interference effects.
Analytics & ExperimentationOnsiteA/B TestingExperimental DesignMetrics - AirwallexMediumData ScientistCompute Moderation View Metrics
An Airwallex Data Scientist onsite SQL question on content-moderation analytics. You write three queries over a per-day view table and a one-to-many post/violation-type policy-score table: distinct posts with 10+ views in the last 7 days, Nudity view prevalence over the last 30 days, and a monthly violation-type distribution. The core challenge is windowed aggregation plus avoiding view double-counting across violation types.
Data Manipulation (SQL/Python)Onsite - AirwallexMediumData ScientistDiagnose a dip in approval/conversion rate
This Analytics & Experimentation prompt evaluates time-series diagnostics, ratio decomposition (numerator/denominator and traffic mix), segmentation and data-quality triage, and causal inference skills at a staff-level applied analytics abstraction.
Analytics & ExperimentationOnsite - AirwallexEasyData ScientistDiagnose conversion-rate time series and CTA swap
This question evaluates a data scientist's competency in time-series diagnosis, conversion-rate decomposition, causal inference, and A/B experiment design within the Analytics & Experimentation category of Data Science.
Analytics & ExperimentationOnsite - AirwallexMediumData ScientistHow do you resolve partner conflict?
This question evaluates a data scientist's cross-functional collaboration, stakeholder management, conflict resolution, influencing without formal authority, and data-driven communication skills.
Behavioral & LeadershipOnsite - AirwallexMediumData ScientistHow would you measure billboard impact?
This problem tests your ability to design a quasi-experimental framework for measuring the offline impact of an OOH (Out-of-Home) advertising campaign on digital conversion metrics.
Analytics & ExperimentationOnsiteCausal InferenceMetricsExperimental Design - AirwallexMediumData ScientistHow would you test swapping two CTA buttons?
This question evaluates A/B test design, causal inference, funnel metric selection and diagnostic/guardrail thinking within Analytics & Experimentation for a Data Scientist role, and is commonly asked to gauge the ability to reason about user behavior, conversion trade-offs, and risk management in sensitive domains like fintech.
Analytics & ExperimentationOnsite - AirwallexMediumData ScientistInvestigate Harassment Surge and Mitigation
An Airwallex Data Scientist onsite analytics question: a monthly moderation report shows the Harassment violation type spiking, and you must decide whether the surge is real or a measurement artifact, then propose and test mitigations. It probes diagnostic analytics, prevalence-metric design, denominator effects, Simpson's paradox, selection bias, label drift, model calibration, causal inference (ITS/DiD), and experiment design with guardrails.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistAnalyze Top 10 Items' Revenue Contribution by Category
This question evaluates proficiency in data manipulation and analytical querying—aggregation, window functions, ranking and revenue attribution—alongside conceptual knowledge of normalization versus denormalization and ETL pipeline design.
Data Manipulation (SQL/Python)Onsite - AmazonHardData ScientistAnalyze an A/B test over last 7 days
This question evaluates competency in A/B test analysis and experiment design, covering conversion-rate computation, statistical inference (two-proportion testing and confidence intervals), sample-ratio checks, heterogeneity assessment (fixed versus random effects), and power/MDE reasoning.
Analytics & ExperimentationOnsite - AmazonEasyData ScientistAnswer Amazon Leadership Principle Scenarios
This question evaluates behavioral competencies relevant to a Data Scientist intern, including leadership, communication, decision-making under uncertainty, risk assessment, time management, teamwork, learning agility, and motivation for the role.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistAnswer core behavioral questions for data roles
Answer core behavioral questions for data roles evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistAssess Culture Fit Through Behavioral Interview Questions
Evaluates culture-fit behavioral answers for inventing and simplifying, customer communication, and innovation. Strong answers use STAR, show stakeholder judgment, quantify impact, and reflect on lessons.
Behavioral & LeadershipOnsite - AmazonHardData ScientistBuild a package-allocation model for couriers
This question evaluates a candidate's ability to design an end-to-end ML-driven package-to-courier allocation system, testing competencies in predictive modeling (per-stop service-time forecasting), constrained combinatorial optimization for routing and fairness, feature engineering, simulation, and production concerns such as cold-start handling and distribution shift. It is asked to assess how candidates balance competing objectives like on-time delivery versus fairness under real-world constraints (shift hours, vehicle capacity, delivery windows, time-varying travel forecasts), and is categorized under Machine Learning with a focus on practical application-level system design rather than purely conceptual theory.
Machine LearningOnsite - AmazonMediumData ScientistCalculate 7-Day Rolling Average for Energy Consumption
This question evaluates programming fluency in numerical data processing and algorithmic implementation for computing moving averages and handling edge cases in time-series sequences, and it applies to Data Scientist roles while falling under the Coding & Algorithms category.
Coding & AlgorithmsOnsite - AmazonMediumData ScientistCalculate A/B sample size, CI, decision rules
This question evaluates skills in experimental design and statistical inference, including sample size calculation, hypothesis testing, confidence interval interpretation, multiple comparison control, variance reduction techniques, and operational considerations for A/B testing.
Statistics & MathOnsite - AmazonMediumData ScientistChoose Effective Graphs for Data Exploration
Evaluates chart selection for exploratory data analysis across categorical and numerical variables. Strong answers explain pie charts, distributions, comparisons, relationships, missingness, and when visuals mislead.
Analytics & ExperimentationOnsite - AmazonHardData ScientistCompare Tableau live vs extract and filters
This question evaluates competency in BI data architecture and large-scale dashboard performance, covering extract vs live connections, refresh and incremental strategies, query pushdown versus in-tool processing, row-level security, filter order-of-operations, performance tuning, and platform migration mapping within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess trade-off reasoning across data freshness, storage, query performance and security for a 100M-row fact table, combining practical application-level decisions (implementation and tuning) with conceptual understanding (filter ordering, calculations, and migration pitfalls) when moving between BI platforms.
Analytics & ExperimentationOnsite - AmazonMediumData ScientistCompute daily work hours from in/out events
This question evaluates a candidate's ability to manipulate time-series punch-event data, including temporal pairing of in/out events, splitting shifts across calendar days, rounding and aggregating hours, and detecting data-quality issues such as overlaps or orphaned events using SQL or Python.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistCompute join counts and window ranks
This question evaluates SQL data-manipulation and analytic competencies including join semantics, set operations, window functions, view versus materialized view trade-offs, key and partition design, and query-tuning techniques in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Onsite - AmazonMediumData ScientistDemonstrate Amazon LP with deep follow-ups
This question evaluates behavioral and leadership competencies for a Data Scientist role, focusing on customer orientation, ownership, accountability for missed deadlines, and responsiveness to negative feedback while requiring quantified impact, scope, dates, and trade-offs.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistDemonstrate Leadership and Ownership in Energy Analytics Role
Amazon-style data scientist behavioral prompt for an energy analytics role, covering Ownership, STAR storytelling, resume walkthrough, transferable analytics skills, quantified impact, and motivation for the domain.
Behavioral & LeadershipOnsite - AmazonHardData ScientistDemonstrate Leadership in Challenging Situations and Decision-Making
Demonstrate Leadership in Challenging Situations and Decision-Making evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AmazonHardData ScientistDemonstrate leadership in data-driven scenarios
This question evaluates leadership and behavioral competencies alongside core data-science skills such as metric design, statistical validation, experimentation, root-cause analysis, stakeholder management, and cross-functional ownership within a Behavioral & Leadership and Data Science domain.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistDemonstrate leadership under disagreement
This question evaluates leadership competencies in stakeholder management, influence without authority, quantitative risk communication, and decision-making under deadline within a Behavioral & Leadership context for a Data Scientist role.
Behavioral & LeadershipOnsite - AmazonMediumData ScientistDemonstrate problem-solving under resistance
This question evaluates a data scientist's end-to-end problem-solving, cross-functional leadership, stakeholder management, and ability to drive measurable business impact under organizational resistance, covering experimental design, data validation, risk assessment, trade-off decisions, and adoption/verification metrics.
Behavioral & LeadershipOnsite
Adobe
Airbnb
Airwallex
Amazon