Data Scientist Interview Questions

Showing 721–750 of 1076 results

• OpenAIMedium·Data Scientist
  Write SQL to compute signup and retention lift

  This prompt evaluates SQL proficiency in time-windowed cohort analysis, event deduplication, date arithmetic, and per-variant aggregation for A/B experiments, categorized under Data Manipulation (SQL/Python) and targeted at an intermediate-level Data Scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• OptiverHard·Data Scientist
  Analyze EV, arbitrage, and bet sizing across games

  This question evaluates proficiency in probability and statistics—particularly expected value and variance computation, arbitrage detection across payoff tables, quantitative bet-sizing strategies (e.g., Kelly and fractional sizing), and numerical approximation skills for independent discrete outcomes.

  Statistics & MathTechnical Screen
• OptiverMedium·Data Scientist
  Compute expected coin flips to meet on octagon

  Compute expected coin flips to meet on octagon evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• OptiverMedium·Data Scientist
  Expected Wait for a Bus That Runs Late Half the Time

  This question evaluates a candidate's understanding of probabilistic modeling and expectation calculations for stochastic arrival processes, touching on renewal-theory concepts and length-biased sampling phenomena relevant to timing and queuing problems.

  Machine LearningTechnical Screen
• OptiverMedium·Data Scientist
  Make Two Apple Piles Equal With Doubling Moves

  This question evaluates algorithmic problem-solving and discrete-mathematics competencies, focusing on reasoning about state transitions, invariants, and number-theoretic constraints within the Coding & Algorithms domain for data scientist roles.

  Coding & AlgorithmsTechnical Screen
• OptiverHard·Data Scientist
  Market-Making Estimation Game: Optimal Confidence-Interval Strategy Over 5 Rounds

  This question evaluates probabilistic reasoning, decision-theoretic optimization of confidence intervals, and sequential risk–reward trade-offs by requiring interval estimation under a nonstandard scoring rule, relevant to a Data Scientist and categorized in Machine Learning/statistical decision theory.

  Machine LearningTechnical Screen
• OptiverMedium·Data Scientist
  Prove a Tight Lower Bound on a Latin Square's Lower-Triangle Sum

  This question evaluates combinatorial reasoning and discrete optimization skills—specifically understanding permutations in Latin squares, triangular-sum inequalities, and construction of extremal examples—within the Software Engineering Fundamentals domain for a Data Scientist position, and it primarily probes conceptual understanding with a constructive application component. Such problems are commonly asked to assess the ability to derive global bounds from local constraints and to exhibit matching constructions, signaling mathematical maturity and facility with combinatorial identities.

  Software Engineering FundamentalsTechnical Screen
• OracleMedium·Data Scientist
  Count Closed Islands

  This problem tests your ability to navigate a 2D grid using graph traversal algorithms like DFS or BFS while identifying connected components that do not touch the grid boundaries.

  Coding & AlgorithmsTechnical ScreenDFSBFSGraph
• OracleMedium·Data Scientist
  Count Interior Islands After Flooding

  This question evaluates understanding of grid-based graph traversal and connected-component detection, focusing on flood-fill logic and the treatment of boundary-connected regions in a binary grid.

  Coding & AlgorithmsTechnical Screen
• OracleEasy·Data Scientist
  Count closed islands in a grid

  This problem tests your ability to navigate a 2D grid using graph traversal algorithms like DFS or BFS while handling specific boundary constraints to identify closed regions.

  Coding & AlgorithmsTechnical ScreenDFSBFSMatrix
• OracleMedium·Data Scientist
  Explain Medical AI Data and Evaluation

  This question tests the ability to design an end-to-end evaluation and data pipeline for a clinical conversational agent, emphasizing safety, bias mitigation, and rigorous validation in a high-stakes environment.

  Machine LearningTechnical ScreenLLMRAGHealthcare AI
• OOtherMedium·Data Scientist
  Build SQL pivot with lookups and currency conversion

  This question evaluates proficiency in data manipulation using SQL or SQL-like Python transforms, covering point-in-time currency conversion, temporal joins for FX lookups, joins to enrich product/customer metadata, aggregation and pivot-style reporting, ranking, and data-quality checks for missing FX.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• 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
  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 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
• 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
• 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
• PayPalHard·Data Scientist
  Compute Variance from a Python List

  This question evaluates understanding of basic statistical concepts and numerical computation—specifically population versus sample variance—and tests algorithmic implementation skills, edge-case handling, and complexity reasoning within the Coding & Algorithms domain.

  Coding & AlgorithmsTechnical Screen
• PayPalEasy·Data Scientist
  Compute variance of a list in Python

  This question evaluates understanding of statistical measures and numeric computation, focusing on implementing variance calculations (population vs sample) and considerations for numerical stability.

  Coding & AlgorithmsTechnical Screen
• PayPalHard·Data Scientist
  Design an A/B for ATO rule

  This question evaluates a data scientist's experiment-design and statistical-analysis competencies, including cluster-aware randomization, power/sample-size calculation, sequential monitoring, and translating detectable effects into net business impact for a real-time account-takeover prevention rule on a payment platform.

  Analytics & ExperimentationTechnical Screen