Data Scientist Interview Questions

Showing 1321–1350 of 1971 results

• OpenAIMedium·Data Scientist
  Design Schema for Accurate Subscription State Tracking

  This question evaluates proficiency in temporal schema design and event-level data modeling, focusing on reconstructing time-varying subscription state using SQL and Python in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Online Assessment
• OpenAIHard·Data Scientist
  Design a free-month experiment

  This question evaluates experimental design, causal inference, metric selection, bias mitigation, ROI estimation, and stakeholder communication skills within the Analytics & Experimentation domain for a Data Scientist role.

  Analytics & ExperimentationTechnical Screen
• OpenAIHard·Data Scientist
  Design and Defend a Free-Trial Conversion Analysis

  Walk through how to plan, execute, and defend a free-trial conversion analysis end to end: cohort definition, data-quality checks, standards for a credible insight, and a recommendation deck that survives live pushback.

  Analytics & ExperimentationOnsiteexperimentationmetricscase-study
• 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
• OpenAIEasy·Data Scientist
  Evaluate a free-trial A/B test

  This question tests your ability to design an A/B test for a freemium product, interpret statistical results, and address potential biases like cannibalization or selection effects.

  Analytics & ExperimentationTechnical ScreenA/B TestingMetricsRetention
• OpenAIHard·Data Scientist
  Handle repeated churn in SQL

  This problem tests your ability to handle complex temporal data by identifying user churn and re-subscription events, then calculating metrics like tenure and churn count per user.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsTime Series
• OpenAIMedium·Data Scientist
  How would you evaluate a free-trial A/B test?

  Evaluates skills in A/B test design and causal inference within the Analytics & Experimentation domain, emphasizing precise metric definition (including guardrails), choice of analysis population (ITT vs per‑protocol), handling of post‑treatment selection, and interpretation of binary conversion and retention metrics; the abstraction level is technical and oriented toward intermediate-to-senior data scientists who must combine statistical rigor with product impact thinking. Commonly asked because it tests the ability to balance acquisition versus downstream retention and revenue, to identify typical analysis pitfalls (time-window errors, incorrect joins/denominators, leakage, peeking, and selection bias), and to translate experiment results into a business recommendation linked to value metrics like LTV and payback period.

  Analytics & ExperimentationTechnical Screen
• OpenAIMedium·Data Scientist
  Identify Bugs in Python Script for User Assignment

  This question evaluates a candidate's ability to detect bugs and data-quality risks in a Python script, testing competencies in code review, reproducible randomization, idempotency, logging, duplicate-exposure safeguards, and safe experiment-assignment logic.

  Coding & AlgorithmsOnline Assessment
• OpenAIMedium·Data Scientist
  Implement NumPy neural-network layers

  This question tests your ability to implement fundamental neural network components using NumPy, focusing on matrix operations, broadcasting, and numerical stability.

  Machine LearningOnsiteNumPyNeural NetworksDeep Learning
• OpenAIHard·Data Scientist
  Measure free-month promotion impact

  This question tests the ability to design an experiment for a subscription product, focusing on causal inference, handling right-censoring in cohort data, and calculating the Return on Investment (ROI) for a promotional offer.

  Analytics & ExperimentationTechnical ScreenCausal InferenceExperiment DesignROI
• OpenAIHard·Data Scientist
  Write SQL for repeat churn

  This question evaluates proficiency in event-time analytics and cohort churn metric computation using SQL, focusing on correct identification of first qualifying events and aggregation by experimental variant.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• OptiverEasy·Data Scientist
  Compute probabilities and expectations in random processes

  This question evaluates proficiency in probability theory, combinatorics, and mathematical expectation through a series of rapid-fire brain-teasers. It tests the ability to set up clean probabilistic models, apply the right technique, and derive exact closed-form answers under time pressure — skills commonly assessed in quantitative and data science interviews.

  Statistics & MathOnline Assessment
• 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
• OptiverHard·Data Scientist
  Find missing numbers in sequences

  This problem tests pattern recognition and algorithm design for identifying missing terms in a sequence that may follow arithmetic, geometric, polynomial, or linear recurrence rules.

  Coding & AlgorithmsOnline AssessmentAlgorithmsPattern RecognitionNumerical Analysis
• OptiverHard·Data Scientist
  Find next terms in sequences

  This question evaluates pattern recognition, numerical reasoning, and sequence-inference competency across arithmetic, fractional, and recurrence-style relationships, and falls under the Statistics & Math domain relevant for data scientist roles.

  Statistics & MathOnline Assessment
• OptiverHard·Data Scientist
  How to rank statements by likelihood

  This problem tests your ability to translate statistical distributions and categorical data into ranked probabilistic likelihoods, accounting for uncertainty and sample size variance.

  Machine LearningOnline AssessmentProbabilityStatisticsBayesian Inference
• 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
  Match alphanumeric patterns in a stream

  This question evaluates skills in exact and approximate alphanumeric string matching, streaming pattern lookup, noise-tolerant comparison, and performance-oriented data structures and encodings.

  Coding & AlgorithmsOnline Assessment
• OptiverMedium·Data Scientist
  Minimize moves to transform floor layouts

  This question evaluates algorithm design and combinatorial optimization skills, focusing on reasoning about minimal moves, greedy heuristics, and proving optimality within the Coding & Algorithms domain for data-science technical roles.

  Coding & AlgorithmsOnline Assessment
• 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
• OptiverHard·Data Scientist
  Solve probability and expectation problems

  This problem tests your ability to calculate expected stopping times in a discrete random walk and your understanding of conditional expectation and symmetry in probability.

  Statistics & MathOnline AssessmentProbabilityExpectationStochastic Processes
• 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