Data Scientist Interview Questions

Showing 31–60 of 1971 results

• 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
• AirwallexMedium·Data Scientist
  Diagnose 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
• AirwallexEasy·Data Scientist
  Diagnose 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
• AirwallexMedium·Data Scientist
  How 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
• AirwallexMedium·Data Scientist
  How 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
• AirwallexMedium·Data Scientist
  How 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
• AirwallexMedium·Data Scientist
  Investigate 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
• 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 CapitalHard·Data Scientist
  Find minimum swaps to sort array with duplicates

  This question evaluates array manipulation and permutation-based reasoning for minimizing swap operations, including handling of duplicate values and performance considerations, within the Coding & Algorithms domain for a Data Scientist role.

  Coding & AlgorithmsOnline Assessment
• 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
• AmazonMedium·Data Scientist
  Analyze 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
• AmazonHard·Data Scientist
  Analyze 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
• AmazonEasy·Data Scientist
  Answer 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
• 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
• AmazonMedium·Data Scientist
  Answer 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
• 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
  Assess 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
• AmazonMedium·Data Scientist
  Build DID panel and compute effects in SQL

  This question evaluates proficiency in panel data construction and causal inference workflows, specifically difference-in-differences estimation, SQL aggregation for user-week metrics, and propensity-score matching implemented in Python.

  Data Manipulation (SQL/Python)HR 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
• AmazonHard·Data Scientist
  Build 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
• AmazonMedium·Data Scientist
  Calculate 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
• AmazonMedium·Data Scientist
  Calculate 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
• 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
• AmazonMedium·Data Scientist
  Calculate Monthly Revenue from Orders in 2023

  This question evaluates proficiency in SQL-based data manipulation, including aggregation, date extraction, and combining transactional tables to produce business metrics, and falls under the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Weekly, Monthly Hours Watched by Premium Users

  This question evaluates a candidate's ability to perform time-based aggregation and data manipulation in SQL and Python, including grouping, filtering by subscription status, and converting minute-level metrics into hours.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonHard·Data Scientist
  Choose Between Fine-Tuning and RAG for Client Chatbot

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Choose Between Fine-Tuning and RAG for Client Chatbot states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• AmazonMedium·Data Scientist
  Choose 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
• AmazonHard·Data Scientist
  Choose Models for Imbalanced Data and Time-Series Forecasting

  Choose Models for Imbalanced Data and Time-Series Forecasting evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen