Data Scientist Interview Questions

Showing 91–120 of 163 results

• Point72Hard·Data Scientist
  Build the auction status table

  This problem tests advanced SQL skills, specifically the ability to perform window functions, handle state transitions, and aggregate time-series data to reconstruct the final status of entities.

  Data Manipulation (SQL/Python)Online AssessmentSQLWindow FunctionsData Aggregation
• Point72Hard·Data Scientist
  Explain and tune decision trees robustly

  The question evaluates a candidate's understanding of CART decision tree mechanics, split criteria and surrogate splits for missing values, hyperparameter tuning and pruning, overfitting diagnostics, preprocessing for large feature sets and high‑cardinality categoricals, and criteria for choosing ensemble methods within the Machine Learning domain.

  Machine LearningOnline Assessment
• Point72Easy·Data Scientist
  Find kth missing integer and redundant operations

  This paired question evaluates array and algorithmic skills — specifically order-statistics and missing-number reasoning for the k-th missing positive, and time-window aggregation with frequency tracking for detecting redundant log events — within the coding & algorithms domain.

  Coding & AlgorithmsOnline Assessment
• Point72Hard·Data Scientist
  Implement composition and mixin utilities

  This problem tests proficiency in functional composition, Python metaclass/decorator patterns, and custom serialization logic, focusing on handling flexible arguments and object introspection.

  Coding & AlgorithmsOnline AssessmentPythonFunctional ProgrammingSerialization
• Point72Hard·Data Scientist
  Price a coin-doubling game rationally

  This question evaluates understanding of probability distributions, divergent expected values, utility theory, and optimal betting strategies (Kelly staking) within the Statistics & Math domain for a Data Scientist role.

  Statistics & MathOnline Assessment
• QTQuantbot TechnologiesHard·Data Scientist
  Build a Data-Cleaning and Machine-Learning Pipeline Without Leakage

  Design schema-aware cleaning, appropriate data splits, training-only fitted transformations, reusable inference state, and validation for missing or unseen inputs.

  Machine LearningOnline Assessment
• QTQuantbot TechnologiesHard·Data Scientist
  Implement GLM Training and Prediction from an Explicit Family and Link

  Explain a from-scratch generalized linear model through family and link selection, consistent loss and gradients, numerical stability, and prediction semantics.

  Machine LearningOnline Assessment
• Qube Research & TechnologiesHard·Data Scientist
  Implement and Tune KNN Classifier

  This question evaluates a candidate's competency in implementing and tuning the K-nearest neighbors algorithm, covering data preprocessing, distance-based classification, model evaluation, and hyperparameter selection.

  Machine LearningOnline Assessment
• RobloxHard·Data Scientist
  Apply Bayes' rule to compute posterior

  Evaluates understanding of Bayes’ rule and basic probability concepts—specifically computing a posterior probability P(A|B) and the marginal likelihood P(B) from a prior and likelihoods—in the Statistics & Math domain at a foundational-to-intermediate abstraction level.

  Statistics & MathOnline Assessment
• RobloxMedium·Data Scientist
  Choose best/worst actions under OA pressure

  This question evaluates judgment and leadership competencies in data science — including risk assessment, stakeholder impact analysis, integrity under time pressure, and handling failing tests and collaborator input — and falls under the Behavioral & Leadership category.

  Behavioral & LeadershipOnline Assessment
• RobloxEasy·Data Scientist
  Compute A/B test sample size and Bayes posterior

  This question tests the ability to calculate required sample size for a frequentist A/B test using power analysis and to perform a Bayesian update for a Bernoulli process.

  Statistics & MathOnline AssessmentStatisticsA/B TestingBayesian Inference
• RobloxHard·Data Scientist
  Compute DID estimate and pretrend flag

  This Analytics & Experimentation question evaluates basic causal-inference and data-manipulation skills by requiring computation of a difference-in-differences (DID) estimate and a binary pretrend flag from panel-like arrays, at an implementation-level focused on summary statistics and edge-case handling.

  Analytics & ExperimentationOnline Assessment
• RobloxEasy·Data Scientist
  Compute diff-in-diff and pretrend flag

  This question tests the ability to calculate a difference-in-differences (DiD) estimator for causal inference and implement a basic statistical test for pre-intervention trend divergence.

  Analytics & ExperimentationOnline AssessmentCausal InferenceData WranglingStatistics
• RobloxHard·Data Scientist
  Compute minimum sample size for A/B test

  Evaluates the ability to perform sample size and power calculations for a two-sample z-test—including estimating outcome standard deviation from historical continuous data—and is categorized under Analytics & Experimentation for a Data Scientist role at an implementation-level applied statistics abstraction.

  Analytics & ExperimentationOnline Assessment
• RobloxHard·Data Scientist
  Compute sample size and Bayes posterior

  This question evaluates understanding of statistical inference and probabilistic reasoning by combining a frequentist power/sample-size computation for a two-sided two-sample z-test with a basic Bayesian conditional probability calculation.

  Statistics & MathOnline Assessment
• RobloxHard·Data Scientist
  Design leakage-free predictive maintenance pipeline

  This question evaluates a data scientist's competency in designing time-series predictive maintenance pipelines, focusing on temporal feature engineering, leakage prevention and point-in-time joins, handling late-arriving labels, class imbalance and cost-sensitive thresholding, probabilistic calibration, explainability, and operational drift monitoring. It is commonly asked in the Machine Learning domain to assess an applicant's ability to produce an end-to-end, production-ready workflow that balances practical implementation concerns with conceptual system-design reasoning, so the task is primarily practical application with important conceptual elements.

  Machine LearningOnline Assessment
• RobloxHard·Data Scientist
  Fit logistic regression and return top features

  This question evaluates understanding of binary logistic regression, coefficient-based feature importance, and practical model-fitting considerations such as intercept inclusion and input shape handling.

  Machine LearningOnline Assessment
• RobloxEasy·Data Scientist
  Implement four DS coding tasks

  This multi-part question evaluates a data scientist's competencies in statistical inference (sample size and z-test calculations), causal inference and parallel-trends validation (difference-in-differences), Bayesian probability updating, and interpretable supervised learning feature importance, all framed as coding tasks.

  Coding & AlgorithmsOnline Assessment
• RobloxMedium·Data Scientist
  Optimize assembly-line scheduling with changeovers

  This question evaluates combinatorial optimization and scheduling competencies, specifically reasoning about NP-hardness, sequence-dependent setup times, precedence and downtime constraints, and designing exact and heuristic solvers for makespan minimization.

  Coding & AlgorithmsOnline Assessment
• SIG (Susquehanna)Easy·Data Scientist
  Calculate Expected Payoff from Two Dice

  This problem tests fundamental knowledge of probability, expected value, and partitioning sample spaces to calculate the average outcome of a stochastic process.

  Statistics & MathOnline AssessmentProbabilityExpected ValueStatistics
• SIG (Susquehanna)Easy·Data Scientist
  Compute Conditional Age Probability Among Cyclists

  This question tests the ability to interpret a contingency table and calculate conditional probabilities by narrowing the sample space to a specific sub-group.

  Statistics & MathOnline AssessmentProbabilityStatisticsContingency Tables
• SIG (Susquehanna)Medium·Data Scientist
  Compute ruin probability with bold betting

  This question evaluates understanding of probability theory and stochastic processes, focusing on ruin probabilities under a bold betting strategy with state-dependent wagers.

  Statistics & MathOnline Assessment
• SIG (Susquehanna)Easy·Data Scientist
  Compute stockout sufficiency probability

  This question evaluates understanding of discrete probability and binomial models for independent Bernoulli trials, testing the ability to compute the probability that a limited resource meets random demand.

  Statistics & MathOnline Assessment
• SIG (Susquehanna)Easy·Data Scientist
  Compute the Probability Two Bloom Intervals Overlap

  Calculate the probability that two independent events with different known durations overlap within a fixed time window using geometric probability.

  Statistics & MathOnline AssessmentProbabilityContinuous DistributionsGeometric Probability
• SIG (Susquehanna)Easy·Data Scientist
  Condition on a Chosen Rabbit Losing

  Calculate the conditional probability that a randomly selected rabbit is the least athletic of three rabbits given that it lost its benchmark race, using Bayes' theorem.

  Statistics & MathOnline AssessmentProbabilityBayes' TheoremConditional Probability
• SIG (Susquehanna)Easy·Data Scientist
  Count Cookie Flavor Combinations

  Calculate the number of distinct unordered combinations when selecting 5 cookies from 4 available flavors with replacement, and contrast this with the ordered outcome space.

  Statistics & MathOnline Assessmentcombinatoricsstars-and-barsprobability
• SIG (Susquehanna)Easy·Data Scientist
  Count Grid Paths with No Three Equal Moves

  Count lattice paths from `(0, 0)` to `(5, 5)` that never use three identical directions consecutively. The solution converts paths into alternating runs of length one or two, counts bounded compositions for equal and unequal run totals, and derives 84 valid paths.

  Statistics & MathOnline Assessment
• SIG (Susquehanna)Easy·Data Scientist
  Deduce Cat Counts from Conditional Truth Rules

  Deduce the exact number of cats owned by three individuals based on conditional truth-telling rules, percentage relations, and average constraints.

  Statistics & MathOnline AssessmentLogic PuzzleNumber TheoryIntegrality
• SIG (Susquehanna)Easy·Data Scientist
  Determine a Canoe Departure Time

  This problem tests algebraic modeling and systems of linear equations by requiring the candidate to derive the counts of different entities based on a total sum constraint.

  Statistics & MathOnline AssessmentAlgebraLinear EquationsLogic
• SIG (Susquehanna)Medium·Data Scientist
  Determine departure time from travel times

  This question evaluates a candidate's understanding of relative motion, time-distance-rate relationships, algebraic modeling, and quantitative reasoning. Commonly asked in technical interviews for Statistics & Math and Data Scientist roles, it assesses the practical application of setting up and manipulating equations from multi-step word problems rather than purely conceptual theory.

  Statistics & MathOnline Assessment