Data Scientist Interview Questions

Showing 1471–1500 of 1971 results

• PlaystationMedium·Data Scientist
  Design Experiments Under Network Interference

  This problem tests your understanding of causal inference in networked environments, focusing on how to detect and mitigate bias when treatment effects spill over between connected users in a social gaming platform.

  Analytics & ExperimentationTechnical ScreenCausal InferenceA/B TestingNetwork Science
• PRPlymouth Rock Assurance CorporationMedium·Data Scientist
  Calculate Ultimate Loss by Policy Term

  This question evaluates a candidate's ability to perform sequence-aware cumulative aggregation and partitioned data manipulation, assessing competency in calculating running totals (ultimate loss) across policy-term records.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• Point72Medium·Data Scientist
  Classify relationships for multiple circle pairs

  This question evaluates computational geometry skills, careful integer arithmetic for numeric precision, case-based classification logic, and analysis of time and space complexity in the Coding & Algorithms domain for Data Scientist roles.

  Coding & AlgorithmsTechnical Screen
• Point72Medium·Data Scientist
  Convert Dates to Calendar Quarter Labels in SQL/Python

  This question evaluates a candidate's ability to perform date/time data transformation and normalization in SQL or Python, focusing on converting raw numeric dates into standardized quarter labels for aggregation and reporting.

  Data Manipulation (SQL/Python)Technical Screen
• Point72Medium·Data Scientist
  Determine Circle Intersection Status for Rendering Order

  This question evaluates geometric reasoning and computational-geometry competency, including spatial relationship analysis and handling numerical edge cases relevant to pairwise object interaction.

  Coding & AlgorithmsTechnical Screen
• Point72Medium·Data Scientist
  Explain How L1 and L2 Regularization Change Linear-Model Coefficients

  This question tests the conceptual understanding of how L1 and L2 penalties influence the optimization objective and the resulting structure of the weight vector in a linear regression model.

  Machine LearningOnsiteRegularizationLinear RegressionOptimization
• 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
  How would you explain PCA and SHAP?

  A Point72 Data Scientist technical screen asking you to defend one end-to-end ML project: problem framing and label timing, leakage controls, time-based validation, feature engineering, hyperparameter tuning, dimensionality reduction with PCA, and model interpretability with SHAP. Expects the PCA optimization objective and SVD link, the Shapley principle behind SHAP, plot interpretation, and the pitfalls of each.

  Machine LearningTechnical Screen
• 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
• Point72Medium·Data Scientist
  Maximize outfits with distinct colors

  This question evaluates competency in combinatorial optimization and constrained resource-allocation algorithms, along with analysis of time and space complexity and robustness to skewed input distributions and edge cases.

  Coding & AlgorithmsTechnical Screen
• 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
• Point72Medium·Data Scientist
  Write SQL for top student per department

  This question evaluates proficiency in SQL query formulation, including group-wise selection, joins, NULL handling, deterministic tie-breaking and ordering to identify a top-per-group student.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• QQuoraMedium·Data Scientist
  Test a Post-Creation Tab While Accounting for User Interference

  Design an experiment for a post-creation tab with creator–reader spillovers, cluster-aware analysis and an evidence-based launch decision.

  Analytics & ExperimentationTechnical Screen
• RedditEasy·Data Scientist
  Collaborate with PM and Eng as DS

  This question evaluates a data scientist's cross-functional collaboration, prioritization, ownership, stakeholder management, and influencing skills when engaging with product managers and engineers.

  Behavioral & LeadershipOnsite
• RedditHard·Data Scientist
  Communicate and de-risk a non-experimental launch

  This question evaluates a data scientist's competency in interpreting causal-inference results from a Synthetic Control, communicating evidence strength and residual risks to executives and skeptical partners, structuring rollout governance and rollback criteria, and managing bias control, dissent and cross-functional accountability.

  Behavioral & LeadershipTechnical Screen
• RedditEasy·Data Scientist
  Design A/B test for AI chat box

  This question tests the ability to define metrics, design an A/B testing framework, and account for potential biases when deploying a new AI-powered chat feature.

  Analytics & ExperimentationOnsiteA/B TestingProduct AnalyticsMetrics
• RedditEasy·Data Scientist
  Design experiment for ads in chat with budgets

  This question tests the ability to design an A/B test for a new ad placement in a chat feature while accounting for budget constraints and potential network effects.

  Analytics & ExperimentationOnsiteA/B TestingCausal InferenceExperiment Design
• RedditHard·Data Scientist
  Estimate Incremental Search-Ad Revenue Without an A/B Test

  Design an evaluation framework to estimate the incremental search-ad revenue of a small-scale manual pilot, incorporating variance reduction, inference under heavy-tailed revenue, and ecosystem cannibalization.

  Analytics & ExperimentationOnsiteexperimentationcausal-inferencead-tech
• RedditHard·Data Scientist
  Justify synthetic control and handle inference

  This question evaluates understanding of synthetic control methodology, causal identification assumptions, estimation and regularization choices, inference techniques for pointwise and cumulative effects, and diagnostic checks for panel time-series interventions.

  Statistics & MathTechnical Screen
• RedditEasy·Data Scientist
  Measure impact of ads-manager automation feature

  This question tests your ability to design an evaluation framework for a new feature, specifically focusing on causal inference, metric selection, and identifying potential biases in a two-sided marketplace.

  Analytics & ExperimentationOnsiteA/B TestingCausal InferenceMetrics
• RedditEasy·Data Scientist
  Prioritize competing engineering requests

  This question evaluates a data scientist's prioritization, stakeholder management, trade-off communication, and leadership competencies when balancing competing analytics and experimentation requests.

  Behavioral & LeadershipOnsite
• RedditMedium·Data Scientist
  Query US-based users and most active forums

  This question evaluates SQL data manipulation skills, including time-based filtering, joins, aggregations, null-safe handling, ratio computation, distinct counts, and top-N ranking to determine user residency and forum activity.

  Data Manipulation (SQL/Python)Technical Screen
• RevolutEasy·Data Scientist
  Define Ultra success metrics and detect suspicious transactions

  This question evaluates a data scientist's ability to define primary, diagnostic, and guardrail metrics for a product launch and to design rule-based suspicious-transaction detection using available schema, emphasizing metric definition, defensible proxy selection, SQL-based data modeling, and financial-crime domain knowledge.

  Analytics & ExperimentationTechnical Screen
• RobinhoodMedium·Data Scientist
  Create OHLC Aggregates from Tick Data in Python

  This question evaluates a candidate's ability to perform time-series bucketing and OHLC aggregation from tick data, testing skills in data parsing, stateful aggregation, and handling missing-interval forward-filling; it falls under Data Manipulation (SQL/Python) and assesses practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Onsite
• RobinhoodMedium·Data Scientist
  Design Real-Time Analytics Pipeline with Kafka and Flink

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Design Real-Time Analytics Pipeline with Kafka and Flink states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite