Data Scientist Interview Questions

Showing 391–420 of 1076 results

• Fetch RewardsMedium·Data Scientist
  Evaluate Overlapping Shelf Ranking Experiments

  This question evaluates competency in experimental design and causal inference for A/B testing, focusing on handling overlapping traffic, interaction effects between concurrent experiments, metric selection, guardrails, and statistical validity.

  Analytics & ExperimentationTechnical Screen
• Fetch RewardsMedium·Data Scientist
  Handle unresponsive stakeholders and align metrics

  This question evaluates stakeholder management, metric design and alignment, ambiguity handling, and decision-making communication skills for a data scientist.

  Behavioral & LeadershipTechnical Screen
• Fetch RewardsMedium·Data Scientist
  Write SQL to verify quarterly sales decline

  This question evaluates proficiency in SQL aggregation, time-series period-over-period comparison, same-store cohort analysis, correct grain handling, and treatment of returns, testing data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• FigmaMedium·Data Scientist
  Describe impact, prioritization, and stakeholder management

  Describe impact, prioritization, and stakeholder management evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• FigmaHard·Data Scientist
  Describe your most and least engaging work

  This question evaluates self-awareness, communication, motivation, collaboration, and role-alignment competencies that reveal which problem types and team environments enable a data scientist to perform best.

  Behavioral & LeadershipTechnical Screen
• FigmaHard·Data Scientist
  How would you measure product success?

  This question tests the ability to define a comprehensive measurement framework for a B2B SaaS collaborative tool, balancing growth, engagement, and retention metrics.

  Analytics & ExperimentationTechnical ScreenProduct AnalyticsMetrics DesignSaaS
• FHFlatiron HealthMedium·Data Scientist
  Build a robust ML pipeline

  This question evaluates a candidate's competency in designing robust end-to-end ML pipelines, covering temporal data slicing, leakage controls, time-series cross-validation, feature store consistency, offline and business metrics, drift detection and monitoring, online rollout strategies, retraining triggers, and fairness assessment.

  Machine LearningTechnical Screen
• FHFlatiron HealthMedium·Data Scientist
  Compute churn metrics and rank top students

  This question evaluates proficiency in data cleaning, transformation, aggregation, and deterministic ranking across R (tidyverse) and SQL, focusing on handling missing values, date arithmetic, aggregation and rounding of metrics, and top-N selection with tie-breaking.

  Data Manipulation (SQL/Python)Technical Screen
• FHFlatiron HealthMedium·Data Scientist
  Write SQL window functions for streaks

  This question evaluates proficiency with SQL window functions, date arithmetic, gap-and-island (streak) detection, rolling aggregates, and event-order sequencing to derive DAU, revenue metrics, and inactivity gaps.

  Data Manipulation (SQL/Python)Technical Screen
• GleanEasy·Data Scientist
  Compute DAU and rolling MAU with zero days

  This question evaluates a data scientist's proficiency in time-series analytics and data aggregation, focusing on computing daily active users and a 30-day rolling MAU from user and login tables, and falls under the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Analyze Linear Regression Changes with Duplicated Observations

  Evaluates regression and hypothesis-testing interpretation under duplicated observations and large samples. Strong answers show unchanged OLS coefficients, standard-error effects, p-value meaning, and practical chi-square adjustments.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Analyze User Flags and Review Outcomes for Moderation Prioritization

  This question evaluates a data scientist's proficiency in SQL-based data manipulation, including joins, distinct counting and grouping, aggregations, string concatenation for user identity, and NULL handling across event and review tables.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Analyze data duplication effects in linear regression

  This question evaluates understanding of OLS estimation, the effects of duplicated observations on estimator variance and inference, and the interplay between sample size, p-values, and effect-size measures within the Statistics & Math domain for a Data Scientist role.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Approximate a percentile from buckets

  This question tests the ability to interpolate values within histogram buckets to estimate a specific percentile, requiring an understanding of linear versus log-space assumptions.

  Statistics & MathTechnical ScreenStatisticsEstimationAlgorithms
• GoogleMedium·Data Scientist
  Assess Fundamental Statistics Knowledge in Data-Science Interviews

  Google data scientist statistics prompt covering MLEs for normal mean and variance, conditional density from a joint pdf, standard normal pdf and cdf, and assumptions for unbiased estimators.

  Statistics & MathTechnical Screen
• GoogleHard·Data Scientist
  Boost Google Workspace Chat Usage with Strategic A/B Testing

  Boost Google Workspace Chat Usage with Strategic A/B Testing evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• GoogleMedium·Data Scientist
  Build Classifier: Evaluate with AUROC for Imbalanced Data

  Evaluates classifier design for detecting dead links in imbalanced URL data with leakage-safe validation. Strong answers cover leakage-safe features, grouped validation, AUROC versus accuracy, PR metrics, thresholding, deployment, and monitoring.

  Machine LearningTechnical Screen
• GoogleMedium·Data Scientist
  Build Model to Predict Customer Contract Renewal

  Evaluates renewal prediction modeling for enterprise Google Meet contracts. Strong answers define leakage-safe timelines, engineer usage, reliability, support, contract, and customer-health features, compare logistic regression with complex models, and evaluate calibration, lift, and business intervention value.

  Machine LearningTechnical Screen
• GoogleMedium·Data Scientist
  Build and evaluate bad-link classifier

  This question evaluates proficiency in applied machine learning classification, including feature design, training a logistic regression, handling severe class imbalance, selecting evaluation metrics and calibration, choosing thresholds under asymmetric costs, and planning offline-to-online validation and monitoring.

  Machine LearningTechnical Screen
• GoogleHard·Data Scientist
  Build and evaluate illegal-video classifier

  This question tests the ability to architect an end-to-end classification pipeline for high-stakes, multimodal content moderation while accounting for class imbalance, adversarial evasion, and operational safety.

  Machine LearningTechnical ScreenMultimodal MLAdversarial MLSystem Design
• GoogleMedium·Data Scientist
  Calculate User Deviation from Team Average Messages

  This question evaluates proficiency with pandas group-wise computations, broadcasting of group aggregates, and DataFrame manipulation without renaming columns.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Clarify ambiguous requirements under pressure

  This question evaluates a data scientist's competency in problem understanding and stakeholder clarification, covering active listening, assumption management, requirement elicitation, and cross-functional communication within the Behavioral & Leadership category.

  Behavioral & LeadershipTechnical Screen
• GoogleHard·Data Scientist
  Compare two stores’ profits rigorously

  This question evaluates a data scientist's skills in experimental design, causal inference (including difference-in-differences), measurement and metric construction for profitability, uncertainty quantification (confidence intervals and power checks), and operational guardrails for real-world data collection.

  Analytics & ExperimentationTechnical Screen
• GoogleMedium·Data Scientist
  Compute precision–recall curve on imbalanced data

  This question evaluates a data scientist's competency in model evaluation for imbalanced binary classification, covering understanding of precision–recall dynamics, calibration, AUPRC, cost‑sensitive metrics, and practical implementation concerns like computing thresholds and handling ties and numerical edge cases; domain: Coding & Algorithms and machine learning model evaluation. It is commonly asked in technical interviews because it assesses both conceptual understanding of evaluation trade‑offs and the practical application of efficient, robust metric computation and threshold selection under asymmetric costs and operational constraints.

  Coding & AlgorithmsTechnical Screen
• GoogleEasy·Data Scientist
  Count super-streak segments in an event stream

  Count super-streak segments in a time-ordered event stream with an O(n) scan. The solution explains maximal segment boundaries, inclusive gap handling, duration and count checks, edge cases, streaming behavior, and changing-threshold follow-ups.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Deduplicate events and rank products with SQL

  This question evaluates proficiency in SQL data manipulation and analytical querying—specifically deduplication using idempotency keys, window functions/CTEs for ranking, distinct aggregation by date, timestamp tie-breaking, and awareness of indexing and scalability trade-offs in large tables; the domain is Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen
• GoogleHard·Data Scientist
  Define and apply Gmail user segments

  A Google data scientist analytics screen: propose actionable Gmail user segments from behavioral metadata, validate their stability and separability over eight weeks, design targeted interventions with metrics and guardrails, and run a privacy-aware experiment to measure per-segment lift. Covers segmentation axes, validation metrics, k-anonymity and differential privacy, CUPED, and RICE roadmap prioritization.

  Analytics & ExperimentationTechnical Screen
• GoogleMedium·Data Scientist
  Demonstrate stakeholder communication and influence

  This question evaluates a candidate's stakeholder communication and influence using data, specifically assessing the ability to present analysis to non-technical partners, manage pushback, make trade-offs, and demonstrate measurable outcomes in a cross-functional decision context for a Data Scientist role.

  Behavioral & LeadershipTechnical Screen
• GoogleMedium·Data Scientist
  Derive MLEs and conditional Normal distributions

  This question evaluates proficiency in parametric inference and probability theory, specifically maximum likelihood estimation, bias of estimators, and conditioning in univariate and bivariate Normal distributions including constrained estimation of parameters.

  Statistics & MathTechnical Screen
• GoogleMedium·Data Scientist
  Describe Overcoming Challenges and Persuading Non-Data Colleagues

  Evaluates behavioral interview storytelling for persuading non-data colleagues and resolving workplace challenges. Strong answers use STAR, translate analysis into business terms, handle objections, show collaboration, quantify outcomes, and reflect on lessons learned.

  Behavioral & LeadershipTechnical Screen