Data Scientist Interview Questions

Showing 211–240 of 641 results

• DoorDashMedium·Data Scientist
  Write complex SQL on DoorDash data

  This question evaluates proficiency in complex SQL querying and data manipulation, including joins across tables, time-based filtering, window functions, null and edge-case handling, aggregations, and integrating promotion and order-event data.

  Data Manipulation (SQL/Python)Onsite
• ExpediaMedium·Data Scientist
  Audit and onboard unfamiliar datasets safely

  This question evaluates a data scientist's competency in dataset discovery, schema and key verification, profiling for distributions/missingness/timezones/currencies, duplicate and join-inflation detection, event-sequence validation, accurate metric computation, and the design of data-quality tests/data contracts within SQL/Python workflows.

  Data Manipulation (SQL/Python)Onsite
• ExpediaHard·Data Scientist
  Choose KPIs and prove impact with experiments

  This question evaluates a data scientist's competency in KPI engineering, causal inference, experiment design, metric validation, and multi-touch attribution within the Analytics & Experimentation domain and is focused on profit-oriented search ranking.

  Analytics & ExperimentationOnsite
• ExpediaHard·Data Scientist
  Validate and monitor ranking model end-to-end

  This question evaluates a data scientist's mastery of learning-to-rank concepts, offline evaluation and metric design, position-bias correction, diagnostic analysis, deployment safeguards, and alignment of surrogate objectives with business KPIs for hotel search ranking.

  Machine LearningOnsite
• FMFannie MaeMedium·Data Scientist
  Design Test Cases for UI Change: Ensuring Quality and Functionality

  Evaluates mission-aware QA collaboration for UI and backend changes in a data-science role. Strong answers compare test layers, define acceptance criteria, cover accessibility and auditability, and design robust test cases.

  Behavioral & LeadershipOnsite
• FMFannie MaeMedium·Data Scientist
  Understand SQL Aggregations and Joins: Key Differences Explained

  This question evaluates a candidate's competency in SQL aggregations (COUNT, SUM, AVG, MIN, MAX), join semantics (INNER, LEFT, RIGHT, FULL OUTER), set operations (UNION vs UNION ALL), window functions, view versus table trade-offs, duplicate detection and removal, query optimization techniques, and pandas DataFrame operations (merge, join, concat) within the Data Manipulation (SQL/Python) domain. It is commonly asked because it assesses both conceptual understanding (function purposes and join semantics) and practical application (writing efficient queries, deduplicating data, and translating patterns between SQL and pandas), thereby probing correctness and performance reasoning.

  Data Manipulation (SQL/Python)Onsite
• FigmaMedium·Data Scientist
  Clarify an Ambiguous Product Question and Define a Metric

  This question tests the ability to transform an abstract product objective into a rigorous measurement framework, focusing on metric selection and experimental design.

  Analytics & ExperimentationOnsiteAnalyticsProduct MetricsExperimentation
• FigmaMedium·Data Scientist
  Handle Cross-Functional Pushback on an Analysis

  This question assesses your ability to navigate professional disagreement, exercise empathy towards cross-functional stakeholders, and leverage data to resolve conflicting viewpoints.

  Behavioral & LeadershipOnsiteBehavioralCommunicationConflict Resolution
• FigmaMedium·Data Scientist
  Present a Relevant Data Science Project to a Hiring Manager

  This question assesses a candidate's ability to communicate technical complexity to a business audience, demonstrate ownership, and articulate the link between statistical results and product outcomes.

  Behavioral & LeadershipOnsiteBehavioralProject-WalkthroughCommunication
• FAFirst AmericanMedium·Data Scientist
  Clarify Ambiguous Requirements and Resolve Team Conflicts

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Clarify Ambiguous Requirements and Resolve Team Conflicts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipOnsite
• Freddie MacMedium·Data Scientist
  Compute leakage-safe rolling features in pandas

  This question evaluates proficiency in time-series feature engineering, leakage prevention, memory-efficient large-scale data processing, categorical target encoding, exponential weighting, and algorithmic complexity within the Data Manipulation (SQL/Python) domain, emphasizing practical application with pandas at scale.

  Data Manipulation (SQL/Python)Onsite
• Freddie MacMedium·Data Scientist
  Implement blocked time-series cross-validation

  This question evaluates competence in designing panel-aware time-series cross-validation with embargo handling, grouped blocking, deterministic splitting, scalability analysis, and adversarial unit-test design for issues like duplicate timestamps and entity migration.

  Coding & AlgorithmsOnsite
• GGeminiMedium·Data Scientist
  Detect k events in a sliding window

  This question evaluates a candidate's ability to design efficient sliding-window and streaming algorithms for time-series event data, encompassing deduplication, handling bounded out-of-order arrivals and clock skew, distinct-device counting, and meeting specified time and space complexity constraints.

  Coding & AlgorithmsOnsite
• GleanMedium·Data Scientist
  Expectation and Variance of a Sum of Two Random Variables, Including Covariance

  Asks how to compute the expectation and variance of the sum of two random variables, in general and when they are independent. Tests derivation from definitions, the role of covariance, generalizing to weighted sums and differences, and applying the result to data-science problems such as comparing group means.

  Statistics & MathOnsite
• GleanEasy·Data Scientist
  How to measure product success?

  This question evaluates a data scientist's competency in product analytics, metric definition, experiment design, and causal inference, including articulation of a north-star metric, supporting funnel and guardrail metrics, and handling confounding, seasonality, and heterogeneous effects.

  Analytics & ExperimentationOnsite
• GleanMedium·Data Scientist
  Product Case: Define Success Metrics for a Product, Using Enterprise Search as Example

  A data scientist product case: define how to measure the success of a product, practiced on an enterprise search tool. Tests building a metric hierarchy with a north star, input metrics and guardrails, separating users from paying customers, defining a successful search and attributing changes causally.

  Analytics & ExperimentationOnsite
• GleanMedium·Data Scientist
  SQL: Zero-Filled DAU, Rolling 30-Day MAU, and the MAU Impact of a User-ID Rehash

  Three SQL questions on a login table: daily active users with zero-filled days, rolling 30-day MAU per date with a weekday-usage follow-up, and how a user-ID rehash inflates MAU. Tests distinct counting, calendar generation, window boundaries, query cost and reasoning about metric artifacts.

  Data Manipulation (SQL/Python)Onsite
• GleanEasy·Data Scientist
  What are sum expectation and variance?

  This question evaluates understanding of expectation, variance and covariance and the ability to reason about how sums of random variables affect their moments, including the role of covariance and the special case of independence in the Statistics & Math domain.

  Statistics & MathOnsite
• GGofundmeMedium·Data Scientist
  Explain an AI Agent You Built: Design, Evaluation, and Governance

  Practice explaining an AI-agent project through design ownership, task evaluation, hallucination controls and data governance.

  ML System DesignOnsite
• 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
• 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
• 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
  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
  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
• 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
  Demonstrate leadership in data ambiguity

  This question evaluates leadership in data ambiguity, assessing competencies in diagnosing underperforming metrics or models, articulating quantified trade-offs, influencing cross-functional stakeholders with data and experiments, and designing operational safeguards and accountability mechanisms.

  Behavioral & LeadershipOnsite
• GoogleMedium·Data Scientist
  Design A/B Test for Google Maps UI Change

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design A/B Test for Google Maps UI Change states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• GoogleMedium·Data Scientist
  Design A/B Test for Subscription Price Increase Effectiveness

  Evaluates experiment design for SaaS subscription pricing and a multivariate sign-up CTA test. Strong answers address pricing-test risks, LTV metrics, factorial design, interaction effects, and guardrails.

  Analytics & ExperimentationOnsite
• GoogleHard·Data Scientist
  Design human review to estimate model accuracy

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Design human review to estimate model accuracy states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathOnsite
• GoogleEasy·Data Scientist
  Design tests to measure latency impact

  A Google Data Scientist onsite case on measuring the causal impact of reduced YouTube video-start latency on engagement. It spans A/B design (primary metric, diagnostics, error/buffering guardrails), randomization unit and interference, power/MDE for heavy-tailed watch time, variance reduction (CUPED, stratification), analysis of conflicting movements, ratio-metric decomposition with Simpson’s paradox, and propensity score matching when randomization isn’t possible.

  Analytics & ExperimentationOnsite