Data Scientist Interview Questions

Showing 661–690 of 1971 results

• DoorDashMedium·Data Scientist
  Model schema and query new-market readiness

  This question evaluates SQL-based data manipulation skills including time-series aggregation, cohort analysis, percentile/median calculations, joins between event and reference tables, and deriving operational KPIs for new-market readiness; it targets the Data Manipulation (SQL/Python) domain for a Data Scientist role.

  Data Manipulation (SQL/Python)Onsite
• DoorDashHard·Data Scientist
  Prioritize projects and manage tight deadlines

  This question evaluates prioritization, scope definition, stakeholder leadership, resource and risk management competencies for a Data Scientist in the Behavioral & Leadership category.

  Behavioral & LeadershipOnsite
• DoorDashEasy·Data Scientist
  Reduce Cold-Food Incidents with Metrics and an Insulated-Bag Experiment

  Design an A/B test and metric framework to evaluate whether equipping couriers with insulated bags reduces cold-food complaints while guarding marketplace efficiency.

  Analytics & ExperimentationTechnical ScreenExperimentationA/B TestingMetrics Design
• DoorDashMedium·Data Scientist
  Resolve Conflicts and Deliver Results Under Pressure

  DoorDash data scientist behavioral prompt on cross-functional disagreement, delivering under limited data or resources, receiving critical feedback, STAR-L storytelling, measurable results, and reflection.

  Behavioral & LeadershipOnsite
• DoorDashMedium·Data Scientist
  Should DoorDash add bicycle dashers?

  This problem tests the ability to design an A/B test for a marketplace intervention, identify success metrics, and account for potential cannibalization and external factors in urban delivery.

  Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace AnalyticsCausal Inference
• DoorDashMedium·Data Scientist
  Solve multi-part SQL with sliding windows

  This question evaluates a candidate's practical SQL data-manipulation skills, including time-based rolling aggregates, distinct counts and deduplication, multi-table joins, revenue aggregation and ranking, and session/streak computations within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Write SQL for deliveries analytics

  This question evaluates SQL data manipulation and analytics skills—specifically aggregation, conditional counting, time-window filtering, joins, and handling of nulls and zero-count periods.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Write SQL for percent and window changes

  This question evaluates proficiency with advanced SQL techniques—specifically the use of CTEs and multiple window functions, row-level versus aggregated conditionals, time-windowed aggregations, generation of unit-day series including zero-order days, and correct handling of missing exposure labels for percent and change calculations.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Write SQL to backtest refund policy

  This question evaluates advanced SQL data-manipulation and analytics competency, including time-based lateness calculations, window functions (PERCENT_RANK and rolling/partitioned windows), CTEs and joins, aggregation, and monetary-field handling for a policy-simulation scenario.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• DropboxMedium·Data Scientist
  Answer rapid-fire leadership checklist

  This question evaluates leadership competencies in privacy decision-making, risk management, stakeholder negotiation, incident prioritization, mentoring, and end-to-end ownership within a data science and operational engineering context.

  Behavioral & LeadershipTechnical Screen
• DropboxMedium·Data Scientist
  Compute max island with constrained flips

  This question evaluates understanding of grid-based graph connectivity, connected-component labeling, and algorithmic complexity analysis (time and space) within the Coding & Algorithms domain, emphasizing practical algorithm design for large-scale inputs.

  Coding & AlgorithmsTechnical Screen
• DropboxMedium·Data Scientist
  Design enterprise file recommendations under ACLs

  This problem tests the ability to design a multi-tenant recommendation system that enforces complex security constraints while maintaining low latency.

  Machine LearningTechnical ScreenRecommendation SystemsMulti-tenancySecurity
• DropboxMedium·Data Scientist
  Explain a project with measurable impact

  This question evaluates a data scientist's ability to demonstrate end-to-end project ownership, measurable impact, causal measurement and metric validity, technical design decisions, risk mitigation, scalability reasoning, and management of security/privacy constraints.

  Behavioral & LeadershipTechnical Screen
• DropboxMedium·Data Scientist
  Run org-safe online experiment for recommender

  This question evaluates a data scientist's competency in online experimentation design, causal inference, clustered randomization, metric definition (primary, secondary, guardrails), sample size/MDE computation, variance reduction and sequential monitoring, instrumentation and privacy-aware logging.

  Analytics & ExperimentationTechnical Screen
• EYMedium·Data Scientist
  Architect cloud data ingestion patterns

  This question evaluates a candidate's ability to design end-to-end cloud data ingestion and serving architectures, including streaming and batch patterns, CDC/event sourcing/micro-batch trade-offs, partitioning and compaction strategies, idempotency, schema evolution, PII tokenization, cost controls, and incident simulation for chaos testing.

  Data Manipulation (SQL/Python)Technical Screen
• EYMedium·Data Scientist
  Design logical model and consumption

  This question evaluates understanding of logical data modeling, ERD-level design with keys and cardinalities, slowly changing dimensions, normalization versus denormalization trade-offs, and strategies for exposing curated datasets and read paths for analytics and regulatory consumers, indicating competency in data modeling and data delivery.

  Data Manipulation (SQL/Python)Technical Screen
• EYMedium·Data Scientist
  Map sources to functional dataset with SQL

  This question evaluates competency in SQL-based data manipulation and data engineering concepts, including handling late-arriving data and deduplication to the latest ingested record, joining and filtering for enrichment and KYC status, and computing exposure metrics such as gross/net notional, limit utilization, and breach flags.

  Data Manipulation (SQL/Python)Technical Screen
• EYMedium·Data Scientist
  Walk through end‑to‑end delivery

  This question evaluates leadership and program-management competencies specific to data science, including end-to-end project ownership, cross-functional coordination, risk identification and mitigation, and production of phase artifacts.

  Behavioral & LeadershipTechnical Screen
• EliseAIMedium·Data Scientist
  Lease Renewal Prediction and Non-Renewal Reason Categorization Take-Home

  A machine learning take-home on predicting lease renewals from tenant conversations and SQLite tables of residents, maintenance work orders and renewal offers. It covers preprocessing, a metric compared with a random baseline, a test-set prediction file, categorizing non-renewal reasons with gte-base embeddings, and presenting the results.

  Machine LearningTechnical Screen
• EEtsyMedium·Data Scientist
  Implement streaming autocomplete under tight memory

  This question evaluates a candidate's ability to design a memory- and latency-constrained autocomplete service, testing skills in data structure selection, string/Unicode normalization, approximate string matching, concurrency, dynamic updates, and algorithmic time/space analysis.

  Coding & AlgorithmsTechnical Screen
• EEvenUpMedium·Data Scientist
  Build a model to predict wine quality

  This question evaluates predictive modeling and data-science competencies—exploratory data analysis, feature assessment, problem framing (regression vs classification vs ordinal), model selection and validation, evaluation metric choice, and post-model feature-importance interpretation—in the Machine Learning domain, combining conceptual understanding with practical application. It is commonly asked in technical interviews for Data Scientist roles because it tests end-to-end modeling judgment, reasoning about data distributions and variable usefulness, selection of validation and evaluation strategies, and awareness of pitfalls such as collinearity and data leakage.

  Machine LearningTechnical Screen
• 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
• ExperianEasy·Data Scientist
  Calculate Expected Flips for Two Heads Coin Toss

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Flips for Two Heads Coin Toss states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• ExperianMedium·Data Scientist
  Compare Spark RDDs, DataFrames, and SQL Performance Gains

  This question evaluates understanding of Spark abstractions (RDDs, DataFrames, Spark SQL), lazy evaluation, memory management, query optimization, and developer ergonomics within the Data Manipulation (SQL/Python) domain of distributed data processing.

  Data Manipulation (SQL/Python)Technical Screen
• ExperianMedium·Data Scientist
  Compare Spark RDDs, DataFrames, and Spark SQL Benefits

  This question evaluates understanding of Apache Spark core abstractions (RDDs, DataFrames, Spark SQL), execution model concepts such as lazy evaluation and optimizer-driven execution, and operational competency with managed cloud cluster job submission and monitoring, categorized under Data Manipulation (SQL/Python) for a Data Scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• ExperianMedium·Data Scientist
  Design Algorithm for Longest Increasing Subsequence Length

  This question evaluates algorithm design and data structure skills, covering subsequence analysis, constraint/graph modeling for seating, and external-memory sorting considerations for scalability.

  Coding & AlgorithmsTechnical Screen
• ExperianMedium·Data Scientist
  Explain PCA and L2 Normalization in Machine Learning

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain PCA and L2 Normalization in Machine Learning states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen