Data Scientist Interview Questions

Showing 61–90 of 339 results

• Capital OneMedium·Data Scientist
  Find top category per region in Aug 2025

  This question evaluates proficiency in SQL data manipulation skills such as joining related tables, aggregating revenue, filtering by date and status, and implementing deterministic tie-breaking for categories.

  Data Manipulation (SQL/Python)Online Assessment
• Capital OneMedium·Data Scientist
  Fix dash dates and aggregate watch time

  This question evaluates data-cleaning and aggregation competencies, focusing on handling nonstandard missing-date markers, forward-filling in spreadsheets, correct date-range filtering, pivot-table summarization, and concise pandas grouping and aggregation.

  Data Manipulation (SQL/Python)Online Assessment
• Capital OneMedium·Data Scientist
  Impute missing values without leakage

  This question evaluates proficiency in data imputation, strict train/validation leakage prevention, temporal per-user propagation rules, and categorical aggregation for model-ready features.

  Data Manipulation (SQL/Python)HR Screen
• Capital OneMedium·Data Scientist
  Merge CSVs and build revenue pivot with pandas

  This question evaluates proficiency in data manipulation and ETL using pandas, covering robust file I/O, join/merge semantics, null/coalescing behavior, date parsing, aggregation, and pivoting.

  Data Manipulation (SQL/Python)Online Assessment
• Capital OneMedium·Data Scientist
  Merge ad CSVs and compute CTR

  This question evaluates SQL data manipulation competencies such as cleaning and merging CSVs, forward-filling missing dates, correct multi-table joins without duplication, use of window functions, time-windowed aggregations, and computation of click-through rate (CTR).

  Data Manipulation (SQL/Python)Online Assessment
• Capital OneMedium·Data Scientist
  Merge seven tables into one clean DataFrame

  This question evaluates proficiency with pandas-based data manipulation, including multi-table joins, aggregations, dtype enforcement, denormalization, and verification of row/column integrity within the Data Manipulation (SQL/Python) domain for a Data Scientist role.

  Data Manipulation (SQL/Python)HR Screen
• Capital OneMedium·Data Scientist
  Reconcile ledgers with SQL/Python and late events

  This question evaluates a candidate's ability to implement data reconciliation and ETL engineering skills, including SQL window functions and joins, time-based FX rate lookups, matching logic for late-arriving events, idempotent daily pipeline design with deterministic keys and exactly-once semantics, and basic statistical charting in Python.

  Data Manipulation (SQL/Python)Technical Screen
• Capital OneMedium·Data Scientist
  Write SQL for lowest price with ratings

  This question evaluates SQL data-manipulation skills including aggregation, conditional filtering, NULL handling, grouping, and deterministic ordering, and sits in the Data Manipulation (SQL/Python) domain with an emphasis on practical application of query writing rather than purely conceptual design.

  Data Manipulation (SQL/Python)Technical Screen
• Capital OneMedium·Data Scientist
  Write SQL for theme-park revenue and visits

  This question evaluates SQL data-manipulation and analytical skills, including joins, aggregations, date-range filtering, handling multiple ticket ownership, and edge cases like zero counts when computing revenue and visit metrics.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Write SQL to compute campaign net revenue

  This question evaluates SQL-based data manipulation and analytics skills, including aggregation, deduplication, date-range filtering, revenue and cost calculations, and selection logic for prioritizing additional donors.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Write SQL to find top net-revenue products

  This question evaluates proficiency in SQL data manipulation, including joins, aggregations, date-window filtering, handling returns and net-revenue calculations, plus the use of analytic/window functions for per-category ranking and tie-breaking.

  Data Manipulation (SQL/Python)Technical Screen
• CCharacter.AIMedium·Data Scientist
  Write SQL for character safety and engagement metrics

  This question evaluates proficiency in SQL-based data manipulation and analytics, specifically aggregations, joins, date-based grouping, ratio calculations, and windowed moving averages to compute safety and engagement metrics.

  Data Manipulation (SQL/Python)Technical Screen
• ChimeMedium·Data Scientist
  Analyze Acquisition Channels for User Value and Retention

  This question evaluates proficiency in SQL/Python data manipulation and analytics, testing aggregation, cohort and retention analysis, ranking, joins and window-function usage along with interpretation of user-level revenue metrics.

  Data Manipulation (SQL/Python)Technical Screen
• ChimeMedium·Data Scientist
  Define and Analyze Early Engagement

  Define an early user activation metric for a fintech platform, write a SQL query to measure monthly cohort activation rates across acquisition channels, and account for lifecycle maturity bias.

  Data Manipulation (SQL/Python)Technical Screensqlcohort-analysisfintech
• ChimeMedium·Data Scientist
  Write SQL for noisy A/B launch metrics

  This question evaluates SQL-based data manipulation and experimental analytics skills, including attribution of earliest exposures, censoring crossovers, session-based bot qualification, time-windowed conversion and revenue calculations, and exclusion of outage periods in PostgreSQL.

  Data Manipulation (SQL/Python)Technical Screen
• ChimeMedium·Data Scientist
  Write rolling-window SQL over weekly cohorts

  This question evaluates proficiency with SQL window functions and CTEs for time-based cohort aggregation, including rolling ROWS-based sums, LAG-based week-over-week percent change, DENSE_RANK ranking, and handling first- and second-purchase calculations.

  Data Manipulation (SQL/Python)Technical Screen
• CCloudTrucksMedium·Data Scientist
  Calculate Average Rate and Cumulative Jobs by State

  This question evaluates competency in relational data manipulation and analytical SQL concepts—including joins, aggregation with DISTINCT counts, per-date averaging, rolling cumulative counts, and handling numeric edge cases—within the Data Manipulation (SQL/Python) domain for a data scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• CoinbaseMedium·Data Scientist
  Compute adoption, latency, and cross-region transactions

  This question evaluates proficiency in SQL-based data manipulation and analytics, including calculating cohort adoption and transaction rates, performing date arithmetic for time-to-first-transaction, identifying cross-region transactions, and computing distributional percentiles.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Data Scientist
  Write SQL and Python for funnels/retention

  This question evaluates SQL and Python data-manipulation skills, including event-to-session joins, temporal window filtering, deduplication, JSON metadata handling, and computation of funnel and retention metrics like daily unique users and conversion rates.

  Data Manipulation (SQL/Python)HR Screen
• CoinbaseMedium·Data Scientist
  Write SQL: sum values ≤ each row’s value

  This question evaluates proficiency in SQL aggregation, query optimization, and handling duplicate values when computing cumulative sums across rows. It is commonly asked to assess practical ability in the Data Manipulation (SQL/Python) domain to design scalable, efficient queries and demonstrates a focus on practical application of query-design and optimization rather than purely conceptual theory.

  Data Manipulation (SQL/Python)Onsite
• ConfluentMedium·Data Scientist
  Determine Tutorial Completion Rate by Variant in 7 Days

  This question evaluates a candidate's competency in data manipulation and event-based analytics, focusing on skills such as joining user and event tables, aggregating event data, and computing cohort completion metrics by variant.

  Data Manipulation (SQL/Python)Onsite
• CoupangMedium·Data Scientist
  Monthly Cohort Retention

  This question tests the ability to transform raw user activity logs into a cohort retention matrix by calculating the time elapsed between a user's first interaction and subsequent activity.

  Data Manipulation (SQL/Python)Technical ScreenSQLRetentionCohort Analysis
• DatabricksHard·Data Scientist
  Find Top-5 Similar Rows

  This question tests the ability to implement a pairwise similarity comparison between rows using a custom distance metric while handling missing data and ranking results.

  Data Manipulation (SQL/Python)Technical ScreenPandasSimilarityData Manipulation
• DatabricksEasy·Data Scientist
  Find top-5 most similar rows across datasets

  This question evaluates understanding and implementation of similarity/distance metrics (e.g., MSE), data preprocessing issues such as handling NULLs and feature scaling, and the ability to perform efficient cross-dataset comparisons and top-k ranking.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• DoorDashMedium·Data Scientist
  Analyze Driver Requests for Food Delivery Orders

  This question evaluates data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain, focusing on window functions for ranking and temporal comparison, joins, and aggregation when analyzing orders and driver requests.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Calculate Late Delivery Percentage and Top Customers

  This question evaluates data manipulation and aggregation skills for a Data Scientist role, focusing on date handling, computing the percentage of late orders, and ranking customers using SQL or Python.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashEasy·Data Scientist
  Calculate Monthly Restaurant Sales Growth

  This problem tests the ability to aggregate transactional data by month and use window functions to calculate percentage growth between consecutive periods while handling null values.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregation
• DoorDashHard·Data Scientist
  Calculate Order Request Metrics

  This question evaluates SQL and data-manipulation competencies for computing order-level metrics, including aggregations, joins, windowed event sequencing, timestamp/timezone handling, and NULL-aware comparisons.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Compute rolling cold-delivery rates with windows

  This question evaluates proficiency with SQL window functions, time-based joins, event deduplication and business-rule implementation for computing rolling metrics over temporal windows.

  Data Manipulation (SQL/Python)Onsite