Data Scientist Interview Questions

Showing 1–16 of 16 results

• AmazonHard·Data Scientist
  Quantify build-vs-buy training decision

  This question evaluates proficiency in quantitative decision analysis, causal inference for experimentation, KPI definition, and risk‑adjusted financial modeling within the Analytics & Experimentation domain, assessing skills relevant for a Data Scientist making operational trade‑offs.

  Analytics & ExperimentationOnline Assessment
• Capital OneMedium·Data Scientist
  Analyze Subscription, Insurance, App, and Card Cases

  This question evaluates product analytics competencies—unit-economics, cohort churn modeling, LTV/CAC and funnel math—requiring clear assumptions, metric definitions, governing formulas, and numeric break-even and conversion calculations.

  Analytics & ExperimentationOnline Assessment
• Capital OneHard·Data Scientist
  Design and analyze ad A/B test

  This question evaluates a candidate's competency in online experimentation and statistical analysis, covering hypothesis formulation, variance reduction, clustering and stratification, multiplicity control, power/MDE calculation, and operational metric guardrails.

  Analytics & ExperimentationOnline Assessment
• Capital OneHard·Data Scientist
  Design and analyze ads A/B test this week

  This question evaluates experimental design and applied statistical analysis skills for A/B testing, covering randomization, exposure control, stratification, variance reduction methods, metric formalization, hypothesis testing, sample sizing, sequential monitoring, diagnostics, and handling delayed offline conversions.

  Analytics & ExperimentationOnline Assessment
• HHBOHard·Data Scientist
  Design an A/B for rare cancellations

  This question evaluates a data scientist's competency in experimental design, rare-event power analysis, precise metric and guardrail definitions, variance-reduction covariate adjustment, sequential-testing decisions, monitoring/dashboard specification, and interpretation of heterogeneous treatment effects.

  Analytics & ExperimentationOnline Assessment
• OneMain FinancialMedium·Data Scientist
  Determine Optimal Marketing Budget Allocation for Maximum Profit

  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 Determine Optimal Marketing Budget Allocation for Maximum Profit states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnline Assessment
• OpenAIMedium·Data Scientist
  Analyze A/B Test Results for Subscription Conversion Rates

  Evaluates A/B test analysis for a limited-time free trial offer. Strong answers compute paid-conversion lift, account for trial starts and churn, test statistical significance, and make a launch decision using retained paid value, revenue, guardrails, and long-term holdouts.

  Analytics & ExperimentationOnline Assessment
• OpenAIMedium·Data Scientist
  Determine Metrics to Measure Free-Trial Impact on Subscriptions

  Determine Metrics to Measure Free-Trial Impact on Subscriptions evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnline Assessment
• RobloxHard·Data Scientist
  Compute DID estimate and pretrend flag

  This Analytics & Experimentation question evaluates basic causal-inference and data-manipulation skills by requiring computation of a difference-in-differences (DID) estimate and a binary pretrend flag from panel-like arrays, at an implementation-level focused on summary statistics and edge-case handling.

  Analytics & ExperimentationOnline Assessment
• RobloxEasy·Data Scientist
  Compute diff-in-diff and pretrend flag

  This question tests the ability to calculate a difference-in-differences (DiD) estimator for causal inference and implement a basic statistical test for pre-intervention trend divergence.

  Analytics & ExperimentationOnline AssessmentCausal InferenceData WranglingStatistics
• RobloxHard·Data Scientist
  Compute minimum sample size for A/B test

  Evaluates the ability to perform sample size and power calculations for a two-sample z-test—including estimating outcome standard deviation from historical continuous data—and is categorized under Analytics & Experimentation for a Data Scientist role at an implementation-level applied statistics abstraction.

  Analytics & ExperimentationOnline Assessment
• WayfairEasy·Data Scientist
  Derive insights and improve complaint resolutions

  This problem tests the ability to join disparate datasets to identify trends, evaluate the effectiveness of resolution strategies, and design an A/B test to validate potential improvements in customer satisfaction.

  Analytics & ExperimentationOnline AssessmentSQLCausal InferenceA/B Testing
• WayfairEasy·Data Scientist
  How to improve complaint resolution

  This question evaluates a candidate's competency in data-driven analytics, causal inference, metrics design, and experimentation for customer support performance.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Evaluate Smart Wait Launch Impact

  This question evaluates a data scientist's competencies in causal inference and experiment analysis, including interpreting A/B test outcomes, trade-offs between conversion and operational metrics, selection of primary and guardrail metrics, and generation of alternative hypotheses.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Interpret a Smart Wait Launch with Conflicting Metrics

  Interpret a product launch where conservative wait estimates reduce conversion while observed pickup time also falls. Diagnose selection effects and alternative causes, define customer and fleet metrics with consistent denominators, and propose an experiment that can separate display accuracy from operational improvement.

  Analytics & ExperimentationOnline Assessment
• WaymoMedium·Data Scientist
  Test Whether a Routing Experiment Reduced Pickup Time

  Analyze a routing A/B test in Python by filtering and joining user and ride data, then calculating a Welch t-test for pickup time. Interpret effect size and uncertainty, address repeated rides and post-treatment selection, and design stronger user-level or cluster-aware inference.

  Analytics & ExperimentationOnline Assessment