Data Scientist Interview Questions

Showing 901–930 of 1971 results

• JJefferiesEasy·Data Scientist
  How to compute correlation from variances

  Practice deriving correlation from variance identities for two random variables. The solution explains why Var(X+Y) and Var(X) alone are insufficient, derives covariance and correlation when Var(Y) is known, simplifies the equal-variance case, and checks edge cases where correlation is invalid or inconsistent.

  Statistics & MathTechnical Screen
• JJefferiesMedium·Data Scientist
  How to derive correlation from variances

  Evaluates derivation of correlation from variance identities for two random variables. Strong answers explain why Var(X+Y) and Var(X) alone do not identify correlation, then derive covariance and correlation when Var(Y) is also known.

  Statistics & MathTechnical Screen
• LLabelboxMedium·Data Scientist
  Explain Google ADK and Compare with LangChain, AWS Bedrock

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain Google ADK and Compare with LangChain, AWS Bedrock states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• LLendingClubMedium·Data Scientist
  Determine If Two Numbers Sum to Target

  This question evaluates a candidate's basic algorithmic fluency in array processing and the ability to reason about pairwise sums using auxiliary data structures.

  Coding & AlgorithmsTechnical Screen
• LLendingClubMedium·Data Scientist
  Query Active Loans by Borrower ID in SQL

  This question evaluates proficiency with SQL data aggregation, grouping, and ordering to compute borrower-level loan totals, assessing practical skills in relational data manipulation within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInHard·Data Scientist
  Analyze homepage drop and feed ranking

  This question evaluates a candidate's competency in product analytics, causal diagnosis, instrumentation and logging validation, segmentation and cohort analysis, and experimental design for feed ranking and user-path behavior.

  Analytics & ExperimentationTechnical Screen
• LinkedInEasy·Data Scientist
  Analyze member video posting behavior by country

  A LinkedIn data scientist technical-screen question on member video-posting behavior using two relational tables. It tests joins, first-event derivation with MIN/GROUP BY, conditional grouping for US vs non-US comparison, and distinct-member counts per country with a length filter.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInEasy·Data Scientist
  Choose better bank queue and describe distributions

  This question tests your ability to apply basic queueing theory to compare waiting times and your ability to reason about how distributional shape affects central tendency measures.

  Statistics & MathTechnical ScreenQueueing TheoryProbabilityStatistics
• LinkedInMedium·Data Scientist
  Compare queueing systems and common distributions

  LinkedIn data scientist statistics screen covering pooled versus separate queues, height distributions and mixture distributions, right-skewed network degree, mean-median-mode ordering, and L1/L2 regularization bias.

  Statistics & MathTechnical Screen
• LinkedInEasy·Data Scientist
  Compute each member’s current notification status

  A LinkedIn data scientist SQL screen: given an end-of-January notification-status snapshot and a February log of turn_on/turn_off actions, compute each member's current status as of 2020-02-29, keeping members from either table. Follow-ups explore reconstructing state when the action log has no date column — unsolvable in general, but recoverable via action-count parity when every action is a guaranteed valid transition.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  Count Article Types Viewed

  Count Article Types Viewed evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  Derive expectation for two consecutive heads

  This question evaluates mastery of probability theory and stochastic processes, specifically expectation and variance calculations, Markov-chain or recurrence reasoning, and pattern waiting times in Bernoulli trials relevant for a Data Scientist role.

  Statistics & MathTechnical Screen
• LinkedInMedium·Data Scientist
  Derive mean and variance of x̄

  This question tests your ability to derive the expected value and variance of the sample mean for a sequence of random variables, accounting for the effect of correlation between them.

  Statistics & MathTechnical ScreenStatisticsVarianceExpectation
• LinkedInHard·Data Scientist
  Design Experiments for Email Campaign & Messaging Update

  Evaluates experiment design for concurrent email and in-product messaging launches with possible interaction effects. Strong answers use factorial designs, power calculations, interaction terms, sequential testing, and guardrails.

  Analytics & ExperimentationOnsite
• LinkedInEasy·Data Scientist
  Design a short-video recommender system

  This question assesses the ability to architect a multi-stage machine learning pipeline for a short-video feed, covering candidate generation, ranking, and performance evaluation.

  Machine LearningTechnical ScreenSystem DesignRecommendationRanking
• LinkedInMedium·Data Scientist
  Device Status without Timestamps

  This question evaluates data manipulation and behavioral analytics competencies, emphasizing aggregation, deduplication, distinct counts, and date-based filtering to measure author and viewer activity.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  Diagnose a Drop in Applications from Job Recommendation Emails

  Diagnose falling applications from job emails by tracing funnel rates, recommendation relevance, promoted-job mix, and ranking changes.

  Analytics & ExperimentationOnsite
• LinkedInEasy·Data Scientist
  Do US members upload more videos than non-US?

  This question evaluates a data scientist's competency in analytics and experimentation, specifically metric design, confounder control, cohorting, and statistical inference for comparing user behavior across segments.

  Analytics & ExperimentationTechnical Screen
• LinkedInMedium·Data Scientist
  Estimate Redesign Impact Using Propensity Score Matching

  Estimate Redesign Impact Using Propensity Score Matching 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
• LinkedInMedium·Data Scientist
  Estimate an Experiment’s Mean Effect from Cohort and Metric Tables

  Estimate experimental impact from cohort and timestamped metric data using user-level outcomes, difference in means, confidence intervals, and valid testing.

  Statistics & MathOnsite
• LinkedInMedium·Data Scientist
  Explain variance reduction in random forests

  This question tests your conceptual understanding of how Random Forests reduce the variance of base decision trees through bagging and feature randomness.

  Machine LearningTechnical ScreenEnsemble MethodsRandom ForestBias-Variance Trade-off
• LinkedInMedium·Data Scientist
  Find index with positive suffix sums

  This problem tests your ability to optimize array traversals using prefix or suffix sums, specifically evaluating your skill in calculating cumulative values efficiently in O(n) time.

  Coding & AlgorithmsTechnical ScreenArraysSuffix SumOptimization
• LinkedInEasy·Data Scientist
  Handle imbalance, validate samples, and avoid overfitting

  This question tests your ability to design a robust machine learning pipeline for a highly imbalanced classification task, focusing on sampling strategies, metric selection, and regularization techniques to prevent overfitting.

  Machine LearningTechnical ScreenImbalanceOverfittingRegularization
• LinkedInEasy·Data Scientist
  How do you sample uniformly from an infinite stream?

  This question tests the implementation of Reservoir Sampling to maintain a uniform random sample of size k from a stream of unknown or infinite length.

  Coding & AlgorithmsTechnical ScreenAlgorithmsProbabilityStreaming
• LinkedInMedium·Data Scientist
  How to analyze Simpson's paradox

  This question evaluates competency in causal reasoning, statistical inference, experimental design, and detection of Simpson's paradox within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• LinkedInHard·Data Scientist
  How to diagnose traffic and measure relevance?

  This problem tests your ability to diagnose an unexplained drop in engagement metrics by decomposing a complex user funnel, isolating root causes using multidimensional data, and designing a robust validation framework.

  Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisMetrics StrategyFunnel Analysis
• LinkedInEasy·Data Scientist
  How would you lead a team to improve quality?

  This question evaluates leadership and technical execution competencies for a data scientist in a tech lead role, including work planning, delegation, accountability, stakeholder communication, root-cause diagnosis of product quality issues, metric definition, and delivering projects with measurable impact.

  Behavioral & LeadershipTechnical Screen
• LinkedInMedium·Data Scientist
  Implement fast sampling for weighted k-sided die

  This question evaluates understanding of probabilistic algorithm design, numerical stability, and efficient data-structure implementation for constant-time sampling from categorical distributions.

  Coding & AlgorithmsTechnical Screen
• LinkedInMedium·Data Scientist
  Implement stream random sampling in Python

  This question evaluates understanding of streaming algorithms, uniform random sampling, and probabilistic guarantees for maintaining a fixed-size sample from an unbounded data stream (reservoir-sampling concepts).

  Coding & AlgorithmsTechnical Screen
• LinkedInMedium·Data Scientist
  Improve Profile Completion Rate

  Evaluates analytics and experimentation strategy for improving LinkedIn profile completion. Strong answers define value-weighted completion metrics, segment gaps, prioritize interventions, and test impact with guardrails.

  Analytics & ExperimentationOnsite