Data Scientist Interview Questions

Showing 271–300 of 339 results

• ShopifyMedium·Data Scientist
  Analyze Pirate Theme Usage Growth Over Time

  This question evaluates a candidate's ability to perform SQL-based data manipulation and time-series analysis, emphasizing joins, aggregation, time-bucketing, and definition of growth metrics to measure feature adoption.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Analyze Pirated Theme Usage

  Calculate monthly metrics on unlicensed theme usage and lost revenue by evaluating temporal interval overlaps between installation lifespans and calendar months.

  Data Manipulation (SQL/Python)Technical ScreenSQLPandasInterval Overlap
• ShopifyMedium·Data Scientist
  Analyze Pirated Theme Usage Impact

  This question evaluates a candidate's ability to perform data manipulation and analytics with SQL and Python to detect license compliance, construct month-level time windows, and quantify revenue impact from pirated theme usage.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Calculate Week-over-Week Revenue Change Using SQL

  This question evaluates proficiency in SQL time-series aggregation, weekly grouping, and prior-period comparison for calculating percentage changes in revenue.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Determine Growth of Pirated Theme Installations Over Years

  This question evaluates proficiency in SQL-based data manipulation and analytics, focusing on computing time-based cohort percentages and conducting year-over-year trend analysis of merchant behavior.

  Data Manipulation (SQL/Python)Onsite
• ShopifyMedium·Data Scientist
  Measure Pirate-Theme Usage and Revenue Growth Over Time

  This question evaluates the ability to design time-based usage metrics and compute monthly revenue through data manipulation using SQL or Python, focusing on aggregating event and transaction records by calendar month.

  Data Manipulation (SQL/Python)Technical Screen
• SnapchatMedium·Data Scientist
  Calculate Average Camera Switches per Session

  Tests the ability to write a PostgreSQL query using window functions (LAG) to compute the average number of specific state transitions per session while accounting for zero-transition sessions and avoiding integer division.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions
• SnapchatMedium·Data Scientist
  Compute CTR and metrics with pandas

  This question evaluates proficiency in time-series and event-level data manipulation with pandas, including CTR computation, event denoising and deduplication, sessionization, cohort analysis, temporal joins, and performance-aware vectorized operations within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• SnapchatMedium·Data Scientist
  Compute User Retention and Analyze Event Data

  This question evaluates a candidate's ability to perform SQL and Python data manipulation for event analytics, including cohort retention, aggregation of distinct purchasing users, median-of-daily-events calculations, and hourly time-series grouping.

  Data Manipulation (SQL/Python)Onsite
• SnapchatMedium·Data Scientist
  Compute same-day acceptance metrics last week

  This question evaluates proficiency in time-based data manipulation, SQL JOINs and aggregations, handling NULLs and missing reference rows, and computing acceptance and percentage metrics with careful UTC date boundary treatment.

  Data Manipulation (SQL/Python)Technical Screen
• SnapchatMedium·Data Scientist
  Count Users Whose First Daily Event Is App Launch

  Write a PostgreSQL query to identify each user's first event per calendar day and aggregate the distinct user count by operating system and device when that first event is an app launch. Tests window functions, tie-breaking logic, and multi-dimensional grouping.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions
• SnowflakeMedium·Data Scientist
  Build a cohort dashboard with Streamlit and SQL

  This question evaluates proficiency in cohort analysis using SQL window functions, time-based aggregation, ARPU calculation, and integration of data querying with a Streamlit visualization front end, including handling timezones and data-quality checks.

  Data Manipulation (SQL/Python)Onsite
• SnowflakeMedium·Data Scientist
  Design an analytic warehouse for event data

  This question evaluates a candidate's competency in data warehousing and engineering for analytics, including event-level schema and partitioning, slowly changing dimensions, idempotent ingestion and deduplication, derived metrics modeling, and query performance and cost trade-offs using SQL/Python.

  Data Manipulation (SQL/Python)Onsite
• SnowflakeMedium·Data Scientist
  Query seven-day conversion with windows and dedupe

  This question evaluates proficiency in time-windowed aggregations, event deduplication, and window-function–based user-level analytics across SQL and Pandas workflows.

  Data Manipulation (SQL/Python)Technical Screen
• StripeMedium·Data Scientist
  Design an idempotent SQL ETL for late data

  This question evaluates practical skills in idempotent ETL design, deduplication by natural keys, handling late-arriving and out-of-order records, upsert/merge strategies, and SQL-based aggregation for daily metrics.

  Data Manipulation (SQL/Python)Technical Screen
• StripeMedium·Data Scientist
  Design metrics and write SQL for a case

  This question evaluates the ability to define time‑bounded, attributable conversion metrics and to implement joins, aggregations, and diagnostic checks in SQL to measure marketing outreach impact.

  Data Manipulation (SQL/Python)Technical Screen
• StripeMedium·Data Scientist
  Write SQL for snapshot features and labels

  This question evaluates proficiency in SQL-based feature engineering for time-series and user-event data, covering snapshotting, temporal joins, deduplication, windowed aggregations, distinct counting, and label construction in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• StripeMedium·Data Scientist
  Write SQL to detect recurring non-subscription users

  This question evaluates a candidate's ability to perform advanced SQL data manipulation and temporal pattern detection in transactional datasets, including attribute-based matching (amount and card fingerprint) and filtering by transaction status.

  Data Manipulation (SQL/Python)Online Assessment
• StripeMedium·Data Scientist
  Write SQL to monitor weekly chargeback spikes

  This question evaluates proficiency in PostgreSQL time-series analytics, specifically aggregations, joins across payments and chargebacks, handling late-arriving events, and calculating per-week metrics such as GMV, active merchants, and chargeback rates.

  Data Manipulation (SQL/Python)Onsite
• TTencentMedium·Data Scientist
  Compute per-player daily cumulative games with windows

  This question evaluates proficiency with SQL window functions, aggregation, date-aware grouping and joins for computing per-player daily counts and running totals, and it falls under Data Manipulation (SQL/Python) for Data Scientist roles, emphasizing practical application rather than purely theoretical concepts.

  Data Manipulation (SQL/Python)Technical Screen
• ThumbtackMedium·Data Scientist
  Compare list/dict; parse JSON/CSV at scale

  This question evaluates understanding of Python data structures (list vs dict), algorithmic time and memory complexity, ordering guarantees in CPython, large-scale parsing and streaming of JSON/CSV, and robust data-cleaning and error-handling strategies.

  Data Manipulation (SQL/Python)Onsite
• ThumbtackMedium·Data Scientist
  Compute weekly 3-week rolling sums in SQL

  This question evaluates time zone–aware date bucketing, gapless calendar generation, and rolling-window aggregation skills, demonstrating competency in SQL-based data manipulation and temporal reasoning.

  Data Manipulation (SQL/Python)HR Screen
• ThumbtackMedium·Data Scientist
  Compute weighted response rates by job category

  This question evaluates a candidate's data manipulation and statistical estimation skills, including pandas-based aggregation, invitation-weighted rate calculation, confidence-interval computation, and robust handling of data-quality issues, in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• TikTokMedium·Data Scientist
  Calculate Conversion Rate from Ad Clicks to Page Visits

  This question evaluates a candidate's competency in event-level data manipulation and metric computation using SQL or Python, including time-based aggregation, deduplication, and joining or windowing of event streams.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Calculate Day-7 Retention Rate from User Post Data

  This question evaluates a candidate's competency in SQL and Python data wrangling, emphasizing date arithmetic, window functions, grouping, and conditional aggregation for cohort and retention calculations.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Calculate User Registration Date and 7-Day Retention Rate

  This question evaluates the ability to perform time-based data manipulation and cohort retention analysis using SQL/Python, including deriving registration dates, aggregating posts within time windows, and computing retention metrics.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Calculate valid daily usage with gap constraints

  This question evaluates a candidate's ability to manipulate temporal intervals, including clipping to a specified day window, merging touching or overlapping segments, detecting gaps, and aggregating minute-level durations using SQL window functions and date/time arithmetic, and belongs to the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Compute 7-day rolling complaint/order ratio in SQL

  This question evaluates SQL data manipulation skills, specifically time-based aggregations, sliding window functions, joins to include entities with no events, and numeric casting within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Compute CTR drop with exclusions

  This question evaluates a candidate's ability to compute time-windowed click-through rates using SQL aggregations, joins, filters and data-cleaning (excluding suspicious ads) while handling edge cases such as missing days, minimum-impression thresholds, division-by-zero, and integer-division pitfalls.

  Data Manipulation (SQL/Python)Onsite
• TikTokMedium·Data Scientist
  Compute and rank top bad advertisers

  This question evaluates proficiency in SQL data manipulation and analytics, including time-window filtering, JOINs, aggregation and deduplication, rate calculations, and window-function–based ranking.

  Data Manipulation (SQL/Python)Technical Screen