Data Scientist Interview Questions

Showing 1–30 of 1076 results

• AAdiaEasy·Data Scientist
  Calculate Playback Counts per Stream

  This question tests the ability to perform relational database joins and use aggregate functions (COUNT) with grouping to summarize event-level data.

  Software Engineering FundamentalsTechnical ScreenSQLJoinsAggregation
• AAdiaEasy·Data Scientist
  Compute Score Difference in Reversing Array Game

  This question tests the ability to manage state changes while iterating through an array based on index-dependent rules, specifically involving conditional array modifications.

  Coding & AlgorithmsTechnical ScreenArraysSimulationDeque
• AffirmMedium·Data Scientist
  Analyze Loan Payments Using Pandas for Key Insights

  This question evaluates data manipulation and aggregation skills using pandas, focusing on computing per-loan payment totals, payment success rates, and a repayment-status flag. It is commonly asked to assess applied data wrangling and numeric summarization abilities in the Data Manipulation (SQL/Python) domain, testing practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• AffirmEasy·Data Scientist
  Calculate Profit of 4-Month Loan at 30% APR

  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 Calculate Profit of 4-Month Loan at 30% APR states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• AffirmMedium·Data Scientist
  Diagnose Sudden Drop in Credit-Card Approval Rate

  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 Diagnose Sudden Drop in Credit-Card Approval Rate states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• AirbnbMedium·Data Scientist
  Aggregate User Activity, Fit Regression, Interpret Coefficients

  This question evaluates skills in relational data aggregation and feature engineering using SQL alongside fitting and interpreting regression models in Python, targeting competencies in transforming event-level user metrics into a modeling dataset and interpreting coefficient estimates.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Build and evaluate an order prediction model

  This question tests the ability to design a robust end-to-end machine learning pipeline for binary classification, focusing on preventing temporal data leakage, managing class imbalance, and aligning model thresholds with business objectives.

  Machine LearningTechnical ScreenClassificationTemporal LeakageModel Evaluation
• AirbnbMedium·Data Scientist
  Build panel in SQL; run causal regression

  This question evaluates proficiency in SQL-based panel data construction, time-series aggregation and rolling-window metrics together with applied causal inference in Python, including two-way fixed-effects difference-in-differences, clustered standard errors, lead tests, and count-model re-specification; it targets Data Manipulation (SQL/Python) skills relevant for Data Scientist roles. It is commonly asked because it combines practical implementation and conceptual understanding—testing temporal joins, treatment timing, avoidance of lookahead/immortal-time biases and late-adopter issues, interpretation of interaction coefficients and confidence intervals, and the ability to reason about identification and model choice.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Compute C/T metrics from bookings and visits

  This question evaluates time-window attribution, visit deduplication, control vs treatment metric computation, aggregation of conversions and revenue, and statistical interval estimation (Wilson CIs), testing practical data manipulation skills with SQL or Python in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Compute browsing metrics in Python from logs

  This question evaluates a candidate's ability to perform event-level data manipulation and analytics in Python and SQL, covering sessionization, time-zone–consistent timestamp handling, deduplication, funnel construction, segment-level aggregation, conversion-rate computation, and statistical confidence intervals such as the Wilson CI.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Define Success Metrics and Experiment Plan for Product Development

  Evaluates product-planning metrics and experiment design for a two-sided marketplace booking funnel. Strong answers define a primary booking metric, diagnostic and guardrail metrics, randomization unit, sample size, marketplace spillovers, and launch criteria before development.

  Analytics & ExperimentationTechnical Screen
• AirbnbMedium·Data Scientist
  Design a network-aware Wi‑Fi badge experiment

  This question evaluates a data scientist's proficiency in experimental design, causal inference under interference (including SUTVA risks), metric specification, power and sample‑size computation, analysis planning, and translating treatment effects into financial decision rules.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Design an A/B test with causal inference

  This question evaluates skills in experimental design, causal inference, and applied statistics — including estimand selection, sample-size calculation under clustering, integrity monitoring, handling noncompliance and contamination, sequential monitoring, and two-proportion inference — within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked because interviewers need to assess the ability to design and analyze robust A/B tests under real-world constraints; the prompt requires both conceptual understanding of causal assumptions and practical application of power calculations, diagnostics, and monitoring procedures.

  Analytics & ExperimentationTechnical Screen
• AirbnbMedium·Data Scientist
  Design and Analyze Airbnb Locker Experiment

  This question evaluates a candidate's skills in experimental design and causal inference for marketplace features, including selecting the appropriate randomization unit, defining eligibility and treatment/control arms, and choosing primary, secondary, and guardrail metrics.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Design robust primary and guardrail metrics

  This question evaluates proficiency in experiment metric design, guardrail definition, data quality diagnostics, and statistical analysis for A/B testing, including power calculations and sequential monitoring, within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Estimate Causal Impact Using Synthetic Control Methods

  Evaluates causal impact estimation after a full product rollout with no holdout. Strong answers use interrupted time series, synthetic control, Bayesian structural time series, exposure variation, and placebo tests to build and validate a credible counterfactual.

  Analytics & ExperimentationTechnical Screen
• AirbnbHard·Data Scientist
  Estimate impact of global launch without holdout

  This question evaluates a candidate's competence in causal inference, observational study design, and statistical uncertainty quantification for product analytics, specifically estimating lift after a global launch without a holdout while addressing confounding, interference, and operational data-quality issues.

  Analytics & ExperimentationTechnical Screen
• AirbnbMedium·Data Scientist
  Influence Decisions Without Direct Authority: Strategies and Outcomes

  Evaluates behavioral storytelling for influencing decisions without direct authority. Strong answers use STAR, map stakeholders, build alignment with shared goals and evidence, handle objections, quantify outcomes, and show how the candidate drove a decision without formal power.

  Behavioral & LeadershipTechnical Screen
• AirbnbHard·Data Scientist
  Lead cross-functional decision without RCT evidence

  This question evaluates a data scientist's cross-functional decision-making, experimental-design reasoning without a holdout, metric definition, and stakeholder-influence skills within a product analytics context.

  Behavioral & LeadershipTechnical Screen
• AirbnbMedium·Data Scientist
  Test conversion difference and adjust for clustering

  This question evaluates proficiency in statistical inference for A/B testing—estimating and comparing conversion proportions, conducting two-sided hypothesis tests, adjusting for day-level clustering using ICC and design-effect corrections, and performing power and sample-size calculations; it belongs to the Statistics & Math domain for a Data Scientist role and combines conceptual understanding with practical application. It is commonly asked to assess an interviewee's ability to interpret conversion uplift under realistic experimental constraints, account for intra-cluster correlation when estimating effective sample sizes and uncertainty, and reason about experiment duration and robustness checks.

  Statistics & MathTechnical Screen
• AirtableEasy·Data Scientist
  Diagnose metric anomalies and evaluate new algorithm

  This question tests your ability to systematically investigate a sudden drop in a key product metric and design a robust A/B test to evaluate the impact of a new recommendation algorithm.

  Analytics & ExperimentationTechnical ScreenA/B TestingProduct AnalyticsRoot Cause Analysis
• AirwallexMedium·Data Scientist
  Diagnose a Structural Break in KYC Submission Rate

  Tests your ability to systematically diagnose sudden metric shifts, secular downward trends, and seasonality in a fintech onboarding funnel using structured hypothesis testing and slice analysis.

  Analytics & ExperimentationTechnical Screenmetric-diagnosisfunnel-analysismix-shift
• Akuna CapitalMedium·Data Scientist
  Count subarrays equal to target

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Count subarrays equal to target states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• Akuna CapitalMedium·Data Scientist
  Maximize profitable pairs

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Maximize profitable pairs states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• AmazonEasy·Data Scientist
  Answer Amazon-style behavioral questions

  This question evaluates interpersonal leadership, communication, and impact-oriented decision-making for a Data Scientist, testing competencies such as handling workplace challenges, motivating teams, and tying experiences to organizational leadership principles.

  Behavioral & LeadershipTechnical Screen
• AmazonEasy·Data Scientist
  Answer core probability and inference questions

  Tests fundamental statistical inference and probability concepts essential for experimentation and data analysis, including the Central Limit Theorem, hypothesis testing errors, and causal reasoning.

  Statistics & MathTechnical ScreenStatisticsA/B TestingHypothesis Testing
• AmazonMedium·Data Scientist
  Append country tables and rank salaries in USD

  This question evaluates skills in data manipulation and integration, specifically appending country-level tables, normalizing salaries via historical exchange rates, deduplicating by a composite primary key, and ranking results, with implementations expected in both SQL and pandas.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Assess Candidate's Alignment with Amazon Leadership Principles

  Evaluates Amazon-style behavioral storytelling for data-science leadership principles such as customer obsession, ownership, dive deep, and earn trust. Strong answers use STAR, metrics, trade-offs, and reflection.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Scientist
  Build DiD dataset with SQL

  This question evaluates proficiency in SQL-based data manipulation and panel construction for staggered-adoption difference-in-differences, testing skills in handling adoption dates, event-time calculations, boundary conditions, and aggregation in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Defect Rate and Identify Top Lanes for Carriers

  This question evaluates competency in SQL-based data manipulation for a data scientist role, covering joins, aggregations, date-based filtering, metric computation, and use of window functions to derive week-over-week changes.

  Data Manipulation (SQL/Python)Technical Screen