Data Scientist Interview Questions

Showing 31–60 of 88 results

• DisneyMedium·Data Scientist
  Write complex SQL for streaming funnels

  This question evaluates a candidate's ability to write complex ANSI-SQL for event-level funnel analysis, including temporal windowing, user attribution, multi-stage aggregation, and handling of event schemas.

  Data Manipulation (SQL/Python)HR Screen
• FigmaMedium·Data Scientist
  Deliver a crisp self-introduction

  This question evaluates a candidate's ability to deliver a concise, metric-backed self-introduction demonstrating product-analytics impact, cross-functional influence, leadership presence, and alignment with a collaborative design SaaS mission.

  Behavioral & LeadershipHR Screen
• FigmaMedium·Data Scientist
  Negotiate Figma compensation analytically

  This question evaluates quantitative compensation analysis, negotiation strategy, and stakeholder communication skills for a Data Scientist role, focusing on modeling offer components and articulating numerical trade-offs.

  Behavioral & LeadershipHR Screen
• GoogleEasy·Data Scientist
  Describe a challenging project and how you succeeded

  This question evaluates a data scientist's ownership, technical depth in research and modeling, decision-making under constraints, stakeholder management, measurable impact orientation, and reflective learning, and it is categorized under Behavioral & Leadership for Data Scientist roles.

  Behavioral & LeadershipHR Screen
• GoogleEasy·Data Scientist
  Design an A/B test for search ranking

  This question tests the ability to design a statistically sound A/B test for a search ranking algorithm, focusing on metric selection, hypothesis formulation, and common pitfalls in experimentation.

  Analytics & ExperimentationHR ScreenA/B TestingMetricsHypothesis Testing
• GoogleEasy·Data Scientist
  Estimate weather’s effect on mental health

  This problem tests the ability to design an observational study to measure the causal impact of meteorological variables on self-reported mental health scores, focusing on confounding variables and identification strategies.

  Statistics & MathHR ScreenCausal InferenceRegressionConfounding
• ImcMedium·Data Scientist
  Adapting a Research Approach Midstream

  This question assesses the candidate's ability to balance technical rigor with business agility, specifically focusing on communication skills, professional resilience, and stakeholder management under pressure.

  Behavioral & LeadershipHR ScreenCommunicationStakeholder ManagementAdaptability
• ImcMedium·Data Scientist
  Managing a Research Project End to End

  This question assesses a candidate's ability to structure open-ended research, manage stakeholder expectations, and maintain scientific rigor while ensuring the final output is actionable for a business audience.

  Behavioral & LeadershipHR ScreenProject ManagementCommunicationAmbiguity
• ImcMedium·Data Scientist
  Motivation for Quantitative Research

  This question assesses the candidate's ability to articulate their intellectual passion for quantitative research by connecting personal motivations to the practical realities of high-frequency trading or data-driven decision-making.

  Behavioral & LeadershipHR ScreenBehavioralQuantitativeResearch
• InstacartMedium·Data Scientist
  Diagnose and fix low conversion rigorously

  This question evaluates a Data Scientist's competency in analytics and experimentation, covering instrumentation validation, funnel and segmentation analysis, causal inference methods (holdouts/DiD), system reliability diagnostics, statistical power and sample-size estimation, and the ability to synthesize findings into executive-level impact statements. It is commonly asked to determine whether a candidate can distinguish measurement or product regressions from marketing-mix shifts and to demonstrate both conceptual understanding of causal inference and practical application of data querying, metric definition, and experiment design in the Analytics & Experimentation domain.

  Analytics & ExperimentationHR Screen
• InstacartMedium·Data Scientist
  Explain handling very large datasets

  This question evaluates a candidate's competence in ingesting and processing very large datasets, covering storage formats and partitioning, memory and compute constraints, schema evolution, data quality checks, indexing strategies, tool selection in SQL/Python ecosystems, and code-level performance optimizations.

  Data Manipulation (SQL/Python)HR Screen
• InstacartMedium·Data Scientist
  Justify Instacart fit and leaving your role

  This question evaluates a data scientist's ability to articulate company and product-market fit, quantify near-term impact, communicate transition logistics, and demonstrate self-awareness through managerial feedback and constraints.

  Behavioral & LeadershipHR Screen
• InstacartHard·Data Scientist
  Lead a zero-to-one initiative effectively

  This question evaluates a data-oriented leader's ability to translate an ambiguous mandate into a measurable initiative by testing skills in problem definition, metric design, discovery and experimentation planning, stakeholder alignment, prioritization, and trade-off reasoning within a two-sided marketplace context.

  Behavioral & LeadershipHR Screen
• IntuitMedium·Data Scientist
  Compute churn and revenue churn in SQL

  This question evaluates a candidate's ability to compute subscription churn and revenue retention metrics from monthly snapshots, exercising SQL data-manipulation competencies such as handling missing or null MRR values, deduplicating snapshot loads, joining across months, and aggregating MRR changes.

  Data Manipulation (SQL/Python)HR Screen
• IntuitMedium·Data Scientist
  Design an idempotent churn ETL pipeline

  This question evaluates a candidate's competence in designing robust ETL pipelines, covering idempotent processing, handling late-arriving streaming subscription events, SCD Type 2 slowly changing dimensions, incremental backfills, data quality checks, upsert/merge semantics, and metric versioning.

  Data Manipulation (SQL/Python)HR Screen
• J.P. MorganMedium·Data Scientist
  Interpret Model Metrics, Regularization, and Resampling Fundamentals

  This problem tests your understanding of foundational machine learning metrics for imbalanced classification and the theoretical utility of regularization and resampling techniques.

  Statistics & MathHR ScreenStatisticsMachine LearningRegularization
• MetaMedium·Data Scientist
  Analyze ad targeting expectations and distributions

  This question evaluates a data scientist's competency in probabilistic modeling, expected-value decision-making, variance decomposition for mixture distributions, and Poisson/exponential arrival-process analysis within the Statistics & Math domain.

  Statistics & MathHR Screen
• MetaHard·Data Scientist
  Choose and compute recommender evaluation metrics

  This question evaluates competency in offline recommender-system evaluation, propensity-weighted policy comparison, classification metric interpretation, probability calibration, model selection trade-offs, and handling data leakage and network interference.

  Machine LearningHR Screen
• MetaHard·Data Scientist
  Design a clustered A/B test with spillovers

  This question tests the ability to design a cluster-randomized experiment when interactions between users (network interference) violate the stable unit treatment value assumption (SUTVA).

  Analytics & ExperimentationHR ScreenCausal InferenceExperimental DesignStatistics
• MetaMedium·Data Scientist
  Write SQL filtering, grouping, CASE, UNION tasks

  This question evaluates proficiency with SQL filtering, aggregation, conditional logic, and set operations—specifically skills around WHERE filtering, GROUP BY/HAVING, CASE bucketing, FLOOR on aggregates, and UNION versus UNION ALL—within the Data Manipulation (SQL/Python) domain and emphasizes practical application of ANSI SQL.

  Data Manipulation (SQL/Python)HR Screen
• Morgan StanleyMedium·Data Scientist
  Describe Your Analysis and Visualization Toolkit

  Tests your ability to articulate an end-to-end data analysis and visualization workflow clearly and concisely for recruiters and hiring managers, connecting technical tool choices directly to business outcomes.

  Data Manipulation (SQL/Python)HR Screendata-analysissqlpython
• Morgan StanleyMedium·Data Scientist
  Explain Your Risk Analytics Work to a Recruiter

  This question tests your ability to translate complex technical risk modeling into a compelling professional narrative that emphasizes business impact, stakeholder collaboration, and personal accountability.

  Behavioral & LeadershipHR ScreenBehavioralRisk AnalyticsStakeholder Management
• Morgan StanleyMedium·Data Scientist
  Implement Factorial and Squares

  A Morgan Stanley data scientist HR-screen live-coding warm-up: implement Python `factorial(n)` (iteratively, then explain the recursive version) and `squares(n)` returning the squares of the first n integers. Evaluates clean Python, the iteration-vs-recursion trade-off, time and space complexity analysis, input validation, and edge cases like n = 0 and negative input.

  Coding & AlgorithmsHR Screen
• Morgan StanleyMedium·Data Scientist
  Solve Market-Risk and Probability Questions

  Morgan Stanley data-scientist HR-screen quant set covering four sub-questions: design a WTI/Brent crude stress-testing framework using heavy-tailed marginals and a t-copula for joint tail dependence; the variance of a sum of correlated normal variables and when the sum is normal; the Monty Hall switching probability; and the eight-balls one-heavier puzzle in two weighings. It probes both conceptual understanding and practical application of dependence modeling, scenario generation, and probabilistic reasoning.

  Statistics & MathHR Screen
• NVIDIAHard·Data Scientist
  Demonstrate cultural fit and sales-oriented leadership

  This question evaluates cultural fit, sales-oriented leadership, customer-facing communication, stakeholder influence, conflict resolution, project failure analysis, product–business alignment, and career trajectory for a Data Scientist role.

  Behavioral & LeadershipHR Screen
• NVIDIAHard·Data Scientist
  Design and explain robust web APIs for ML inference

  This question evaluates a candidate's proficiency in designing HTTP REST APIs for ML inference, including API endpoints, versioning, idempotency, request/response schemas, authentication/authorization, rate limiting, observability, and backward-compatibility policies, and it falls under the Coding & Algorithms domain for a data scientist role.

  Coding & AlgorithmsHR Screen
• NVIDIAHard·Data Scientist
  Diagnose overfitting, DenseNet, preprocessing, CV

  This question evaluates a candidate's understanding of overfitting and mitigation techniques, comparative architecture knowledge between DenseNet and ResNet, leakage-free preprocessing and harmonization for medical imaging, practical CNN hyperparameter tuning, and cross-validation strategies.

  Machine LearningHR Screen
• NVIDIAMedium·Data Scientist
  Explain NVIDIA fit and role value

  This question evaluates a candidate's cultural and role fit, ability to communicate measurable impact, skill in mapping past data-science projects to posted responsibilities, awareness of recent organizational initiatives, and adaptability to team culture.

  Behavioral & LeadershipHR Screen
• NVIDIAHard·Data Scientist
  Implement CUDA-tiled matrix multiplication and explain architecture

  This question evaluates a candidate's competency in CUDA GPU programming, parallel algorithms, and performance engineering for FP32 matrix multiplication, covering tiling strategies, memory hierarchy (global/shared/register), synchronization, numerical precision, and occupancy/resource analysis.

  Coding & AlgorithmsHR Screen
• NVIDIAHard·Data Scientist
  Optimize CUDA GEMM with tiling and coalescing

  This question evaluates proficiency in GPU programming and performance optimization, assessing understanding of the CUDA execution model, memory hierarchy and bank conflicts, occupancy limits, and practical kernel design concerns for single-precision GEMM including tiling, coalescing, vectorized memory accesses and benchmarking.

  Coding & AlgorithmsHR Screen