Data Scientist Interview Questions

Showing 211–240 of 339 results

• NetflixMedium·Data Scientist
  Analyze Retention Metrics Using SQL and Python

  This question evaluates proficiency in SQL time-based cohort analysis and practical Python algorithm implementation, testing retention metric computation using windowed date calculations and an efficient approach for finding two numbers whose sum is closest to zero.

  Data Manipulation (SQL/Python)Other
• NetflixMedium·Data Scientist
  Determine Maximum Consecutive Order Days Per User

  Determine Maximum Consecutive Order Days Per User evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Onsite
• NetflixMedium·Data Scientist
  Write SQL for rolling frequency caps

  This question evaluates proficiency with rolling-window analytics, frequency cap logic, time-based attribution, and per-user/per-campaign aggregation for advertising data.

  Data Manipulation (SQL/Python)Onsite
• NotionMedium·Data Scientist
  Analyze Document Collaboration CSVs

  This problem tests your ability to clean, join, and analyze messy user-activity data using pandas or SQL, focusing on window functions and handling missing values.

  Data Manipulation (SQL/Python)Technical ScreenSQLPandasData Cleaning
• NotionEasy·Data Scientist
  Analyze document collaboration from CSV logs

  This question evaluates data manipulation and analytical competencies—particularly deduplication, joins, aggregation, co-occurrence counting, and metric definition—applied to CSV access logs and user metadata.

  Data Manipulation (SQL/Python)Technical Screen
• NotionMedium·Data Scientist
  Analyze document collaboration patterns

  This question evaluates a candidate's competency in data manipulation and analytics—specifically joins, deduplication of user-document pairs, aggregation, and computation of user- and country-level collaboration metrics using SQL or Python.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIHard·Data Scientist
  Compute churn with re-subscriptions

  This problem tests your ability to reconstruct user subscription states over time from raw event logs, specifically identifying active status to calculate monthly churn rates.

  Data Manipulation (SQL/Python)Technical ScreenSQLData ManipulationTime-series
• OpenAIEasy·Data Scientist
  Compute signup rate and retention from raw logs

  This question evaluates the ability to manipulate event-level logs to compute cohort metrics—specifically signup rate and D30 retention—testing proficiency in SQL/Python, event-time windowing, joins, aggregation, and understanding of upstream logging semantics.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIMedium·Data Scientist
  Debug and harden trial-assignment Python code

  This question evaluates understanding of experiment assignment, data integrity, reliability engineering, concurrency, observability, and privacy in data systems, testing competencies around randomness, idempotency, time semantics, retries, and failure-mode identification.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIMedium·Data Scientist
  Design Schema and Logic for Subscription Event Tracking

  This question evaluates proficiency in event-driven data modeling, temporal state derivation, and implementation of SQL/Python logic to compute per-user subscription status and history within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Online Assessment
• OpenAIMedium·Data Scientist
  Design Schema for Accurate Subscription State Tracking

  This question evaluates proficiency in temporal schema design and event-level data modeling, focusing on reconstructing time-varying subscription state using SQL and Python in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Online Assessment
• OpenAIHard·Data Scientist
  Handle repeated churn in SQL

  This problem tests your ability to handle complex temporal data by identifying user churn and re-subscription events, then calculating metrics like tenure and churn count per user.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsTime Series
• OpenAIHard·Data Scientist
  Write SQL for repeat churn

  This question evaluates proficiency in event-time analytics and cohort churn metric computation using SQL, focusing on correct identification of first qualifying events and aggregation by experimental variant.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIMedium·Data Scientist
  Write SQL to compute signup and retention lift

  This prompt evaluates SQL proficiency in time-windowed cohort analysis, event deduplication, date arithmetic, and per-variant aggregation for A/B experiments, categorized under Data Manipulation (SQL/Python) and targeted at an intermediate-level Data Scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• OOtherMedium·Data Scientist
  Build SQL pivot with lookups and currency conversion

  This question evaluates proficiency in data manipulation using SQL or SQL-like Python transforms, covering point-in-time currency conversion, temporal joins for FX lookups, joins to enrich product/customer metadata, aggregation and pivot-style reporting, ranking, and data-quality checks for missing FX.

  Data Manipulation (SQL/Python)Technical Screen
• OOtherMedium·Data Scientist
  Solve window-function SQL without joins

  This question evaluates proficiency with SQL window functions, including running and cumulative sums, LAG/forward-fill event logic, ranking functions (RANK/DENSE_RANK/ROW_NUMBER), tie-breaking strategies, and temporal date arithmetic for payments and subscription events.

  Data Manipulation (SQL/Python)Technical Screen
• OOtherMedium·Data Scientist
  Write SQL to analyze response accuracy and speed

  This question evaluates proficiency with SQL-based data manipulation and analytical querying on relational assessment data, including aggregation, grouping, joins, windowed ranking, and use of PostgreSQL statistical functions to analyze accuracy and response-time metrics.

  Data Manipulation (SQL/Python)Online Assessment
• OOthersEasy·Data Scientist
  Compute per-coin quarterly amounts and totals

  This question evaluates competence in SQL data manipulation—specifically time-based conditional aggregation, grouping, joins, and handling entities with no activity—to compute per-coin quarterly sums and year-to-date totals.

  Data Manipulation (SQL/Python)Online Assessment
• PayPalMedium·Data Scientist
  Clean and Summarize User Purchase Data Efficiently

  This question evaluates proficiency in data cleaning, aggregation, and time-series manipulation within the Data Manipulation (SQL/Python) domain, focusing on filtering by group counts and calculating inter-event timing metrics.

  Data Manipulation (SQL/Python)Onsite
• PayPalMedium·Data Scientist
  Compare WHERE vs HAVING with aggregates

  Compare WHERE vs HAVING with aggregates evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Onsite
• PayPalMedium·Data Scientist
  Explain Window Functions and Joins in SQL and Python

  This question evaluates proficiency in SQL and Python data manipulation, covering window functions, join semantics (INNER, LEFT, RIGHT, FULL, CROSS), ranking functions, group and string aggregation, type casting, and building row-wise accumulations in Python, and it falls under the Data Manipulation (SQL/Python) category.

  Data Manipulation (SQL/Python)Onsite
• PayPalMedium·Data Scientist
  Identify Session with Maximum Overlapping Sessions Count

  This question evaluates temporal interval reasoning, set-based querying and aggregation skills, focusing on identifying and counting overlapping time intervals using SQL or equivalent Python data-manipulation tools.

  Data Manipulation (SQL/Python)Onsite
• PayPalMedium·Data Scientist
  Identify Users with Specific Page Visit Sequence

  This question evaluates event-sequence detection and temporal ordering skills in SQL, focusing on concepts such as windowing, adjacent-row reasoning, and session-scoped grouping to identify ordered page visits.

  Data Manipulation (SQL/Python)Onsite
• PayPalMedium·Data Scientist
  Write SQL to flag Venmo ATO

  This question evaluates a candidate's ability to construct complex SQL to detect account-takeover (ATO) signals in a payments platform, assessing skills in event-time windowing, deduplication, joins across user/login/transaction tables, and categorical flagging of new devices or IPs.

  Data Manipulation (SQL/Python)Technical Screen
• PayPalEasy·Data Scientist
  Write SQL using HAVING and window functions

  This question tests the ability to filter aggregated data using the HAVING clause and compute relative metrics using window functions to identify high-frequency transaction patterns.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregations
• PayPalMedium·Data Scientist
  Write conditional aggregates with CASE WHEN

  Write conditional aggregates with CASE WHEN evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Onsite
• PayPalMedium·Data Scientist
  Write conditional aggregation SQL queries

  This question evaluates proficiency with conditional aggregation in SQL, including the use of SUM(CASE WHEN ...) versus filtering with WHERE and the implications of placing aggregation filters in HAVING for grouped queries, within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• PinterestMedium·Data Scientist
  Analyze Global Engagement and Impressions with SQL Queries

  This question evaluates proficiency in SQL-based data manipulation and analytical querying, covering aggregation, grouping, ranking, and the correlation of impressions with user engagement and feature usage metrics.

  Data Manipulation (SQL/Python)Onsite
• PinterestMedium·Data Scientist
  Calculate the Percentage of Highly Active Users by Country

  Calculate highly active users by country over seven calendar days using distinct active dates and same-day surface diversity, with percentage output.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Scientist
  Clean and Aggregate Transactions for Finance Dashboard

  This question evaluates data cleaning and transformation competencies including pandas-based vectorized operations, categorical mapping and feature flagging, as well as aggregation and summarization of transactional data in the data manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite