Data Scientist Interview Questions

Showing 721–750 of 1971 results

• Goldman SachsEasy·Data Scientist
  Bound the Rank of BA Given Rank of AB

  Determine the set of possible values for the rank of the matrix product BA given that A and B are 4x4 real matrices and rank(AB) = 2. This question tests fundamental linear algebra inequalities involving matrix ranks and nullities.

  Statistics & MathOnline AssessmentLinear AlgebraMatrix TheoryRank Inequalities
• Goldman SachsEasy·Data Scientist
  Compute the Arc Length of a Curve

  Tests single-variable calculus fundamentals, specifically calculating the derivative, setting up the standard arc length integral, and evaluating it using substitution.

  Statistics & MathOnline Assessmentcalculusintegrationgeometry
• Goldman SachsEasy·Data Scientist
  Count Palindromic Substrings

  This question tests your ability to identify palindromes within a string using expansion around centers or dynamic programming.

  Coding & AlgorithmsOnline AssessmentStringsTwo PointersDynamic Programming
• Goldman SachsEasy·Data Scientist
  Expected Number of Runs in Coin Tosses

  Tests understanding of linearity of expectation and indicator random variables through counting consecutive runs in a sequence of coin tosses.

  Statistics & MathOnline Assessmentprobabilityexpected-valuecombinatorics
• Goldman SachsEasy·Data Scientist
  Infer BA When AB Is the Identity

  This question tests the understanding of matrix invertibility, the properties of the identity matrix, and the fundamental differences between square and non-square matrix multiplication.

  Statistics & MathOnline AssessmentLinear AlgebraMatricesRank
• Goldman SachsEasy·Data Scientist
  Integrate a Two-Sided Exponential Density

  This question tests the ability to integrate probability density functions (PDFs) over specified intervals, specifically handling the absolute value function in symmetric distributions.

  Statistics & MathOnline AssessmentCalculusProbabilityDistributions
• GoogleMedium·Data Scientist
  Address Overfitting in Supervised Learning Models

  Evaluates supervised learning judgment around bias, variance, overfitting, and train-test performance gaps. Strong answers diagnose leakage, split errors, distribution shift, and high variance, then propose regularization, simpler models, validation, more data, and monitoring.

  Machine LearningOnsite
• GoogleMedium·Data Scientist
  Analyze Linear Regression Changes with Duplicated Observations

  Evaluates regression and hypothesis-testing interpretation under duplicated observations and large samples. Strong answers show unchanged OLS coefficients, standard-error effects, p-value meaning, and practical chi-square adjustments.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Analyze User Flags and Review Outcomes for Moderation Prioritization

  This question evaluates a data scientist's proficiency in SQL-based data manipulation, including joins, distinct counting and grouping, aggregations, string concatenation for user identity, and NULL handling across event and review tables.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Analyze data duplication effects in linear regression

  This question evaluates understanding of OLS estimation, the effects of duplicated observations on estimator variance and inference, and the interplay between sample size, p-values, and effect-size measures within the Statistics & Math domain for a Data Scientist role.

  Statistics & MathTechnical Screen
• GoogleHard·Data Scientist
  Analyze time series and design validation experiment

  This problem tests the ability to detect structural changes in time-series data and design a rigorous A/B testing strategy to measure the impact of an intervention on count-based metrics.

  Analytics & ExperimentationOnsiteTime SeriesCausal InferenceA/B Testing
• GoogleEasy·Data Scientist
  Answer Google Behavioral Questions

  A set of four common Google Data Scientist behavioral interview questions covering self-introduction and fit, diagnosing and solving a team problem, communicating a missed deadline to stakeholders, and handling a team event with reluctant participants. The solution uses a STAR/STAR-L framework and explains the competencies Google scores for: collaboration, ownership, communication, judgment, and inclusion.

  Behavioral & LeadershipOnline Assessment
• GoogleMedium·Data Scientist
  Approximate a percentile from buckets

  This question tests the ability to interpolate values within histogram buckets to estimate a specific percentile, requiring an understanding of linear versus log-space assumptions.

  Statistics & MathTechnical ScreenStatisticsEstimationAlgorithms
• GoogleMedium·Data Scientist
  Assess Fundamental Statistics Knowledge in Data-Science Interviews

  Google data scientist statistics prompt covering MLEs for normal mean and variance, conditional density from a joint pdf, standard normal pdf and cdf, and assumptions for unbiased estimators.

  Statistics & MathTechnical Screen
• GoogleHard·Data Scientist
  Boost Google Workspace Chat Usage with Strategic A/B Testing

  Boost Google Workspace Chat Usage with Strategic A/B Testing 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 & ExperimentationTechnical Screen
• GoogleMedium·Data Scientist
  Build Classifier: Evaluate with AUROC for Imbalanced Data

  Evaluates classifier design for detecting dead links in imbalanced URL data with leakage-safe validation. Strong answers cover leakage-safe features, grouped validation, AUROC versus accuracy, PR metrics, thresholding, deployment, and monitoring.

  Machine LearningTechnical Screen
• GoogleMedium·Data Scientist
  Build Model to Predict Customer Contract Renewal

  Evaluates renewal prediction modeling for enterprise Google Meet contracts. Strong answers define leakage-safe timelines, engineer usage, reliability, support, contract, and customer-health features, compare logistic regression with complex models, and evaluate calibration, lift, and business intervention value.

  Machine LearningTechnical Screen
• GoogleMedium·Data Scientist
  Build and evaluate bad-link classifier

  This question evaluates proficiency in applied machine learning classification, including feature design, training a logistic regression, handling severe class imbalance, selecting evaluation metrics and calibration, choosing thresholds under asymmetric costs, and planning offline-to-online validation and monitoring.

  Machine LearningTechnical Screen
• GoogleHard·Data Scientist
  Build and evaluate illegal-video classifier

  This question tests the ability to architect an end-to-end classification pipeline for high-stakes, multimodal content moderation while accounting for class imbalance, adversarial evasion, and operational safety.

  Machine LearningTechnical ScreenMultimodal MLAdversarial MLSystem Design
• GoogleEasy·Data Scientist
  Build next-word predictor with O(1) lookup

  This problem tests your ability to design a memory-efficient data structure for storing n-gram probabilities and performing fast lookups for the most likely next word.

  Coding & AlgorithmsOnline AssessmentHash MapData StructuresNLP
• GoogleMedium·Data Scientist
  Calculate Top Countries' Gmail Usage and MoM Change

  This question evaluates proficiency in SQL data aggregation, time-based grouping, and analytical window functions for computing month-over-month percentage changes in event volumes.

  Data Manipulation (SQL/Python)Onsite
• GoogleMedium·Data Scientist
  Calculate User Deviation from Team Average Messages

  This question evaluates proficiency with pandas group-wise computations, broadcasting of group aggregates, and DataFrame manipulation without renaming columns.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Can bootstrap help reduce variance

  This question evaluates understanding of bootstrap resampling, the distinction between estimating an estimator’s variance and methods that actually reduce estimator variance, and related ensemble reasoning for a Data Scientist role in the Statistics & Math domain.

  Statistics & MathOnsite
• GoogleMedium·Data Scientist
  Clarify ambiguous requirements under pressure

  This question evaluates a data scientist's competency in problem understanding and stakeholder clarification, covering active listening, assumption management, requirement elicitation, and cross-functional communication within the Behavioral & Leadership category.

  Behavioral & LeadershipTechnical Screen
• GoogleMedium·Data Scientist
  Compare Logistic Regression and Random Forest in Limited Data Scenarios

  Evaluates binary classification model selection with limited labeled data. Strong answers explain logistic regression and cross-entropy loss, compare it with Random Forests, cover L1 and L2 regularization, diagnose overfitting, and contrast Random Forests with boosting.

  Machine LearningOnsite
• GoogleHard·Data Scientist
  Compare two stores’ profits rigorously

  This question evaluates a data scientist's skills in experimental design, causal inference (including difference-in-differences), measurement and metric construction for profitability, uncertainty quantification (confidence intervals and power checks), and operational guardrails for real-world data collection.

  Analytics & ExperimentationTechnical Screen
• GoogleMedium·Data Scientist
  Compute precision–recall curve on imbalanced data

  This question evaluates a data scientist's competency in model evaluation for imbalanced binary classification, covering understanding of precision–recall dynamics, calibration, AUPRC, cost‑sensitive metrics, and practical implementation concerns like computing thresholds and handling ties and numerical edge cases; domain: Coding & Algorithms and machine learning model evaluation. It is commonly asked in technical interviews because it assesses both conceptual understanding of evaluation trade‑offs and the practical application of efficient, robust metric computation and threshold selection under asymmetric costs and operational constraints.

  Coding & AlgorithmsTechnical Screen
• GoogleEasy·Data Scientist
  Count super-streak segments in an event stream

  Count super-streak segments in a time-ordered event stream with an O(n) scan. The solution explains maximal segment boundaries, inclusive gap handling, duration and count checks, edge cases, streaming behavior, and changing-threshold follow-ups.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Decide between two vendors under constraints

  This question evaluates a candidate's competency in decision modeling and cost‑sensitive machine learning, including causal inference for selection bias, propensity estimation, constrained optimization for capacity and SLA trade‑offs, and offline/online evaluation in the Machine Learning domain for a Data Scientist role.

  Machine LearningOnsite
• GoogleMedium·Data Scientist
  Deduplicate events and rank products with SQL

  This question evaluates proficiency in SQL data manipulation and analytical querying—specifically deduplication using idempotency keys, window functions/CTEs for ranking, distinct aggregation by date, timestamp tie-breaking, and awareness of indexing and scalability trade-offs in large tables; the domain is Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen