Data Scientist Interview Questions

Showing 871–900 of 1971 results

• 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
• InstacartEasy·Data Scientist
  How to debug an apparent D14 retention drop

  This question evaluates competency in cohort-based retention analysis, understanding metric maturity and right-censoring, performing data quality and definition checks, and localizing real declines across segments, releases, or funnel changes.

  Analytics & ExperimentationTechnical Screen
• InstacartEasy·Data Scientist
  How would you investigate a metric decline?

  This question evaluates a data scientist's competency in diagnostic analytics, metric instrumentation awareness, causal reasoning, and segmentation-driven problem localization for consumer marketplace KPIs.

  Analytics & ExperimentationTechnical Screen
• InstacartHard·Data Scientist
  Improve low R² without p‑hacking

  This question evaluates competence in statistical modeling and causal inference, covering regression diagnostics, feature engineering and interactions, appropriate error distributions and link functions, leakage detection, model selection and validation, and the trade-off between predictive accuracy and valid effect estimation.

  Machine LearningOnsite
• InstacartMedium·Data Scientist
  Investigate Instacart Revenue Decline Using Weekly Data

  Instacart analytics prompt on investigating a 4% weekly revenue decline, covering time-series anomaly detection, seasonality, changepoints, prediction intervals, and richer-data decomposition by orders, AOV, geography, cohorts, and categories.

  Analytics & ExperimentationTechnical Screen
• InstacartEasy·Data Scientist
  Investigate marketplace metrics and experiment rollout

  This question evaluates competency in marketplace analytics, causal inference, experimentation design, metric instrumentation, cohort and retention analysis, and trade-off reasoning between core business metrics and secondary signals.

  Analytics & ExperimentationTechnical 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
• InstacartMedium·Data Scientist
  Recommend and validate a budget allocation strategy

  This question evaluates a data scientist's competency in budget allocation, experimental design, KPI definition, segmentation, and causal inference by requiring translation of SQL-derived spend and profitability insights into constrained program- and advertiser-level budget rules while managing concentration risk.

  Analytics & ExperimentationTechnical Screen
• InstacartMedium·Data Scientist
  Write SQL to rank advertisers and profitability

  This question evaluates a candidate's ability to perform ANSI SQL data transformations including unpivoting denormalized tables, aggregations, joins, and financial metric calculations such as net profit and profit margin while handling nulls and tie-breaking rules.

  Data Manipulation (SQL/Python)Technical Screen
• IEIntercontinental ExchangeMedium·Data Scientist
  Analyze Newton-Raphson Convergence, Choose Sigma, and Derive Vega

  This question evaluates numerical root-finding and option pricing competencies, specifically understanding Newton–Raphson convergence properties, implied volatility inference under the Black–Scholes model, and option sensitivity (Vega).

  Statistics & MathTechnical Screen
• IntuitMedium·Data Scientist
  Build 30-day retention cohort table

  This problem tests your ability to aggregate user activity data to calculate 30-day retention rates, requiring joins, date arithmetic, and windowing functions.

  Analytics & ExperimentationTechnical ScreenSQLRetentionCohort Analysis
• IntuitEasy·Data Scientist
  Build a predictive model from TurboTax sample data

  This problem tests your ability to design an end-to-end predictive modeling pipeline for tax-related data, focusing on feature engineering, handling imbalanced datasets, and selecting appropriate evaluation metrics.

  Machine LearningOnsiteClassificationFeature EngineeringImbalanced Data
• IntuitMedium·Data Scientist
  Build cohort 30-day retention from signup date

  This problem tests your ability to manipulate time-series data to calculate cohort-based user retention rates while handling data censoring constraints.

  Analytics & ExperimentationTechnical ScreenSQLPandasRetention
• 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
  Compute monthly new subscribers and YoY deltas

  This question evaluates proficiency in SQL time-series aggregation and cohort identification, including generating a month spine, computing year-over-year deltas, handling nulls and division-by-zero, and identifying partial months via a data watermark.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitEasy·Data Scientist
  Design an experiment for pricing page redesign

  This question evaluates a candidate's competency in experiment design, causal inference, impact sizing, metric framework development, and statistical analysis in the context of a pricing page redesign that adds an annual subscription option.

  Analytics & ExperimentationTechnical 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
• IntuitMedium·Data Scientist
  Engineer and Impute ZIP Features

  This Intuit data scientist machine learning screen evaluates feature engineering from address/ZIP data, joining external census-style datasets, high-cardinality encoding, and missing-ZIP imputation. It also tests awareness of fairness, privacy, leakage, and how to validate whether geographic features actually improve a model.

  Machine LearningTechnical Screen
• IntuitMedium·Data Scientist
  Exclude free subscribers via anti-join

  This question evaluates competency with SQL anti-joins, join semantics, deduplication, and correct pre-aggregation filtering to prevent excluded entities from affecting aggregated monthly and year-over-year metrics; it is categorized as Data Manipulation (SQL/Python) for a Data Scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitEasy·Data Scientist
  Pivot daily users and revenue by platform

  This question evaluates data manipulation and aggregation skills, specifically computing distinct user counts and revenue aggregates and reshaping results by platform; it is in the Data Manipulation (SQL/Python) domain and assesses practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• J.P. MorganEasy·Data Scientist
  Compute expected value of simple gambling games

  This problem tests your understanding of expected value, probability distributions, and the mechanics of independent trials in simple gambling games.

  Software Engineering FundamentalsTechnical ScreenProbabilityStatisticsExpected Value
• J.P. MorganMedium·Data Scientist
  Explain C++ virtual dispatch and object lifetime

  This question tests the candidate's understanding of C++ memory management, the mechanics of the virtual method table (vtable), and the importance of virtual destructors in polymorphic inheritance.

  Software Engineering FundamentalsOnsiteC++Memory ManagementPolymorphism
• J.P. MorganMedium·Data Scientist
  Explain Overfitting and Transformer Basics

  This question tests your conceptual understanding of model generalization, the mechanics of the Transformer architecture, and the trade-offs involved in sequence modeling.

  Machine LearningTechnical ScreenDeep LearningTransformersBias-Variance Tradeoff
• J.P. MorganMedium·Data Scientist
  Implement Integer Square Root

  This question evaluates numerical algorithm design and integer arithmetic competence, including robustness for edge cases and overflow handling, and is categorized in the Coding & Algorithms domain.

  Coding & AlgorithmsTechnical Screen
• J.P. MorganMedium·Data Scientist
  Implement KNN from Scratch

  This problem tests your ability to implement the K-Nearest Neighbors (KNN) algorithm from scratch, focusing on distance computation, sorting, and majority voting logic.

  Coding & AlgorithmsTechnical ScreenKNNSupervised LearningAlgorithms
• 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
• J.P. MorganMedium·Data Scientist
  Solve tiling, probability, and nested-radical puzzles

  This problem tests your ability to derive recurrence relations for combinatorial tiling, solve infinite series limits, and calculate exact probabilities using combinations.

  Coding & AlgorithmsOnsiteCombinatoricsDynamic ProgrammingProbability
• J.P. MorganMedium·Data Scientist
  Test whether samples follow a binomial distribution

  This problem tests the application of goodness-of-fit testing to verify if a set of discrete observations conforms to a binomial distribution.

  Machine LearningOnsiteStatisticsHypothesis TestingProbability
• JGJain GlobalEasy·Data Scientist
  Define the p-Value, Then Explain How to Test Two Price Series for Cointegration

  Two statistics questions from a systematic trading screen: give a precise definition of the p-value and its common misreadings, then explain what cointegration is and how to test two price series for it. It tests hypothesis-testing rigor, stationarity, the difference between correlation and cointegration, and multiple-testing pitfalls.

  Statistics & MathTechnical Screen