Data Scientist Interview Questions

Showing 1351–1380 of 1971 results

• OOtherEasy·Data Scientist
  Check if a numeric string is divisible by 8

  Determine whether a very large non-negative integer given as a decimal string is divisible by 8 without converting the whole string to an integer. The key insight is that only the last three digits matter (since 1000 is a multiple of 8), giving an O(1) check; a streaming modular-remainder approach is the O(n) alternative.

  Coding & AlgorithmsTechnical Screen
• OOtherMedium·Data Scientist
  Compute and estimate Markov transition probabilities

  This question evaluates understanding of discrete-time Markov chains, multi-step transition probabilities and empirical estimation of transition matrices, testing probabilistic reasoning and matrix-based computation in the Statistics & Math domain for a Data Scientist role; it is commonly asked because it probes both theoretical grasp of stochastic processes and practical data-driven parameter estimation. At an applied algorithmic abstraction level, it requires explicit mapping of state labels to matrix indices, computing k-step transitions from a given row-stochastic matrix, estimating transition probabilities from observed one-step transitions, and addressing zero-outgoing-observation cases and any chosen smoothing policy (or justification for none).

  Statistics & MathTechnical Screen
• OOtherEasy·Data Scientist
  Compute multi-step Markov probabilities and estimate transitions

  This question evaluates understanding of discrete-time Markov chains, computation of multi-step transition probabilities from a one-step transition matrix, and empirical estimation of transition matrices from observed state-pair data including handling states with zero outgoing transitions.

  Statistics & MathTechnical Screen
• OOtherHard·Data Scientist
  Decide launch with asymmetric costs

  This question evaluates a data scientist's ability to integrate statistical inference (p‑values, Type I/II errors, and prior probabilities) with decision analysis and financial impact assessment (NPV, rollout and rollback costs) in the context of A/B testing.

  Analytics & ExperimentationTechnical Screen
• OOtherHard·Data Scientist
  Decide launch with p=0.10 at alpha 0.05

  This question evaluates a data scientist's competency in statistical inference and experiment decision-making, including frequentist hypothesis testing, conditional power and sample-size planning, Bayesian updating, and cost-sensitive risk analysis.

  Statistics & MathTechnical Screen
• OOtherMedium·Data Scientist
  Defend fit and handle pressure in finance interview

  This Behavioral & Leadership interview question evaluates a candidate's ability to translate data science experience into FP&A and finance analytics competencies—quantifying prior impact, handling monthly close and variance analysis, revenue forecasting and headcount/opex tracking, leveraging SQL/Excel, and managing stakeholder pressure.

  Behavioral & LeadershipTechnical Screen
• OOtherHard·Data Scientist
  Define goals and success metrics for subscriber-only features

  This question evaluates a data scientist's competency in product analytics and experimentation, specifically goal selection, success metric definition, ROI calculation, cannibalization analysis, and end-to-end experiment and instrumentation design for subscriber-only features.

  Analytics & ExperimentationTechnical Screen
• OOtherHard·Data Scientist
  Design an A/B test with guardrails

  This question evaluates a data scientist's skills in experimental design, hypothesis testing, statistical power and sample-size calculation, randomization and contamination control, pre-registration planning, and multiplicity adjustment for A/B testing.

  Analytics & ExperimentationOnsite
• OOtherMedium·Data Scientist
  Design anomaly detection and handle imbalanced logistic regression

  Design a fraud detection setup for 50M transactions at a 0.4% fraud rate: a supervised logistic regression model plus an unsupervised anomaly detector for new merchants with no labels. Cover time-based splits, delayed labels, imbalance, calibration, and a cost-based threshold where a missed fraud costs far more than a false alarm.

  Machine LearningTechnical Screen
• OOtherHard·Data Scientist
  Design metrics and experiment

  This question evaluates a candidate's competency in experimental design, success-metric definition, event instrumentation, causal inference for heterogeneous treatment effects, and statistical power and sample-size calculations for subscription-product analytics.

  Analytics & ExperimentationTechnical Screen
• OOtherMedium·Data Scientist
  Design metrics resilient to data quality

  This question evaluates the ability to design robust data-quality metrics and apply SQL window-function techniques for deduplication and time-windowed completeness calculations, focusing on quantifying how ingestion delays, duplicates, and negative amounts can bias payment metrics.

  Analytics & ExperimentationTechnical Screen
• OOtherMedium·Data Scientist
  Diagnose and fix linear regression violations

  Work through an OLS model on 10,000 observations: state the Gauss-Markov assumptions, show why residuals are orthogonal to the predictors, and handle three correlated predictors. Decide which violations break unbiasedness versus only inference, and what fixes apply.

  Statistics & MathOnsite
• OOtherMedium·Data Scientist
  Handle unprofessional, prescriptive interviewers

  This question evaluates interpersonal communication, conflict management, stakeholder negotiation, time management, and the ability to succinctly surface technical and data-quality risks during a constrained SQL screening, reflecting competencies relevant for data scientists in collaborative settings.

  Behavioral & LeadershipTechnical Screen
• OOtherEasy·Data Scientist
  How would you measure and experiment on harmful content?

  This question evaluates a data scientist's competency in measuring and experimenting on harmful user-generated content, focusing on severity quantification, metric selection, and randomized experiment design within the analytics and experimentation domain.

  Analytics & ExperimentationTechnical Screen
• OOtherMedium·Data Scientist
  Implement KNN from scratch

  This question evaluates understanding of the k-Nearest Neighbors algorithm, distance metrics (Euclidean and cosine), tie-breaking with class priors, weighted voting, complexity analysis, and scalability strategies, testing competencies in algorithm implementation, model behavior, and computational trade-offs.

  Coding & AlgorithmsOnsite
• OOtherMedium·Data Scientist
  Implement multiplication without using the multiplication operator

  This question evaluates proficiency in bitwise arithmetic, integer overflow semantics, sign handling, and algorithmic efficiency for implementing multiplication without using multiplication or division operators.

  Coding & AlgorithmsTechnical Screen
• OOtherHard·Data Scientist
  Predict job changes month by month

  This question evaluates competency in discrete-time survival modeling and end-to-end predictive system design, encompassing feature engineering, label/hazard formulation, leakage prevention, model evaluation, fairness assessment, and experimentation for production machine learning.

  Machine LearningOnsite
• OOtherMedium·Data Scientist
  Prove reservoir sampling correctness

  This question evaluates randomized and streaming algorithm skills, including mastery of reservoir sampling, induction-based correctness proofs, probability reasoning, and complexity analysis for sampling under strict memory constraints.

  Coding & AlgorithmsOnsite
• OOtherMedium·Data Scientist
  Separate demand from supply for jeans

  This question evaluates a data scientist's ability to perform data-driven diagnostics, funnel-metric construction, quantitative decision-rule design, and experimentation planning to distinguish demand-side from supply-side constraints in a marketplace jeans category.

  Analytics & ExperimentationOnsite
• OOtherMedium·Data Scientist
  Simulate via inverse transform and Gibbs

  This question evaluates understanding of random variate generation and Markov chain Monte Carlo techniques, specifically inverse-transform sampling for the Logistic(μ,s) distribution and Gibbs sampling for a bivariate joint density, testing competency in probability theory, distribution properties, and statistical computing within the Statistics & Math domain. It is commonly asked to assess the ability to derive inverse CDFs and full conditional distributions, verify sample correctness with goodness-of-fit and convergence diagnostics, and reason about sample size, burn-in, and thinning, reflecting a mix of conceptual understanding and practical application.

  Statistics & MathOnsite
• OOtherMedium·Data Scientist
  Solve window-function SQL without joins

  This question evaluates proficiency with SQL window functions, including running and cumulative sums, LAG/forward-fill event logic, ranking functions (RANK/DENSE_RANK/ROW_NUMBER), tie-breaking strategies, and temporal date arithmetic for payments and subscription events.

  Data Manipulation (SQL/Python)Technical Screen
• OOtherMedium·Data Scientist
  Write SQL to analyze response accuracy and speed

  This question evaluates proficiency with SQL-based data manipulation and analytical querying on relational assessment data, including aggregation, grouping, joins, windowed ranking, and use of PostgreSQL statistical functions to analyze accuracy and response-time metrics.

  Data Manipulation (SQL/Python)Online Assessment
• OOtherMedium·Data Scientist
  Write mini-batch gradient descent

  This question evaluates understanding and implementation of optimization algorithms for machine learning, specifically mini-batch gradient descent, and measures competency in numerical optimization, algorithmic implementation, and training dynamics within the Coding & Algorithms domain for Data Scientist roles.

  Coding & AlgorithmsOnsite
• OOthersEasy·Data Scientist
  Answer ancestor-walk queries on a rooted tree

  This question evaluates understanding of tree data structures and ancestor-walk queries, assessing competency in traversing rooted trees, interpreting parent arrays, and designing efficient query-processing methods for large n and q.

  Coding & AlgorithmsOnline Assessment
• OOthersEasy·Data Scientist
  Compute per-coin quarterly amounts and totals

  This question evaluates competence in SQL data manipulation—specifically time-based conditional aggregation, grouping, joins, and handling entities with no activity—to compute per-coin quarterly sums and year-to-date totals.

  Data Manipulation (SQL/Python)Online Assessment
• PayPalMedium·Data Scientist
  Analyze Transactions for Risk and Implement Mitigation Strategies

  Analyze Transactions for Risk and Implement Mitigation Strategies 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 & ExperimentationOnsite
• PayPalHard·Data Scientist
  Assess card transactions and plan risk strategy

  Assess card transactions and plan risk strategy 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 & ExperimentationOnsite
• PayPalMedium·Data Scientist
  Boost User Login Rate: Key Metrics to Monitor

  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 Boost User Login Rate: Key Metrics to Monitor states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• PayPalHard·Data Scientist
  Build a real-time ATO model

  This question assesses the ability to design an end-to-end, low-latency machine learning pipeline for detecting account takeover (ATO) in payment transactions, focusing on label noise, feature engineering, and system deployment constraints.

  Machine LearningTechnical ScreenReal-time SystemsFraud DetectionFeature Engineering
• PayPalEasy·Data Scientist
  Calculate Probability of Heads in Coin Flip Experiment

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability of Heads in Coin Flip Experiment states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite