Data Scientist Interview Questions

Showing 181–210 of 339 results

• MetaMedium·Data Scientist
  Identify Top 10 Users by Average Call Duration

  This question evaluates SQL-based data manipulation and time-series aggregation skills, specifically computing per-user metrics from timestamped event data such as call durations.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Rank Ads by Conversion Rate for Top 10 Performers

  This question evaluates data manipulation and analytics skills for computing time-windowed ad performance metrics, including aggregations of impressions and revenue, join logic between impression and conversion records, conversion-rate calculation, and ranking.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Resolve Ties for Top-10 Users in SQL Query

  This question evaluates proficiency in SQL ranking and tie-resolution techniques for generating deterministic top-N leaderboards, focusing on handling identical scores while producing an exact set of rows.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Retrieve Ad Metrics and Rates for Last 7 Days

  This question evaluates proficiency in SQL-based data manipulation including joins, conditional aggregation, time-based filtering, and computation of advertising metrics such as impressions, clicks, CTR, and conversion rate.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Retrieve Top Five Ads by Conversions in 30 Days

  This question evaluates a candidate's competency in SQL data manipulation, specifically aggregating event data and applying time-window filters to produce ranked metrics.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL filtering, grouping, CASE, UNION tasks

  This question evaluates proficiency with SQL filtering, aggregation, conditional logic, and set operations—specifically skills around WHERE filtering, GROUP BY/HAVING, CASE bucketing, FLOOR on aggregates, and UNION versus UNION ALL—within the Data Manipulation (SQL/Python) domain and emphasizes practical application of ANSI SQL.

  Data Manipulation (SQL/Python)HR Screen
• MetaMedium·Data Scientist
  Write SQL for CTR and Revenue

  This question tests the ability to join event logs with reference data, perform conditional aggregation to calculate click-through rates (CTR), and apply date-sensitive currency conversions.

  Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationJoin
• MetaMedium·Data Scientist
  Write SQL for Pixel Signal Metrics

  This question evaluates SQL-based data manipulation and analytical competencies, specifically joining and aggregating event and ad performance tables, computing event-weighted metrics and composite data-quality scores (such as validity and match-rate based measures) across a 30-day window.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for comment analytics

  This question evaluates proficiency in SQL analytics, specifically joins, filtering, aggregation (distinct counts), percentile functions, time-windowed grouping, handling soft-deleted records, and writing efficient queries via CTEs.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL for daily chats and fast replies

  This question evaluates a candidate's ability to manipulate relational data and reason about temporal event sequences, specifically testing skills in SQL aggregation, distinct counting, deduplication, bidirectional relationship handling, and time-windowed event matching.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL for multi-account metrics

  This question tests your ability to perform complex aggregations across multiple tables, specifically using conditional logic and joins to compute user engagement metrics.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for retention, conversion, and churn

  This question evaluates proficiency in SQL-based analytics and time-aware data manipulation, covering cohort and retention computation, conversion and churn metrics, user-level deduplication across platforms, timezone-local day handling, rolling-window comparisons, and calculation of statistical confidence intervals.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL for seller and category metrics

  This question tests the ability to perform complex aggregations, handle date filtering, and calculate performance ratios while managing potential division-by-zero errors in a multi-table SQL environment.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• MetaMedium·Data Scientist
  Write SQL for social feed metrics and ties

  This question evaluates advanced SQL analytics skills and competency in data modeling for social feed metrics, including relationship joins, viewer-day temporal aggregation, weighted interaction scoring, and per-user top-N ranking.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL to analyze shop visibility

  This question evaluates proficiency in SQL-based time-series and event-data manipulation, including deduplication of consecutive states, window functions, aggregation, cohort analysis, and percentile/summary statistics.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to compare exclusive category engagement

  This question evaluates SQL-based data manipulation and cohort analysis competencies, including cohort definition, session-level filtering and aggregation for metrics such as user counts, median active days, proportions, and per-user average minutes while applying data-quality constraints over a fixed date window.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to flag coordinated fake accounts

  This question evaluates proficiency in SQL-based data manipulation, temporal windowing, event-table joins, aggregation and feature engineering for fraud detection, including computing weighted risk_score flags across user activity.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write SQL to infer group-call demand

  This question evaluates SQL-based data manipulation and product-analytics competencies, including temporal sessionization, graph connectivity inference for detecting overlapping 1:1 call loops, deduplication trade-offs (UNION vs UNION ALL), and computation of derived metrics to estimate latent feature demand within the Data Manipulation (SQL/Python) domain. It is commonly asked because it measures practical application of event-time windowing, connected-component reasoning and edge-case handling (e.g., excluding failed connections) for producing daily summary metrics, emphasizing hands-on SQL proficiency over purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write SQL/pandas for KPI anomaly

  This question evaluates proficiency with SQL window functions and equivalent pandas operations for KPI anomaly detection, covering baseline computation using same-weekday medians over eight weeks, per-country and per-post_type Likes-per-DAU metrics with deduplication of users for DAU, outage filtering, attribution of declines between new and existing users, and time-series comparisons like friend-request acceptance rates while explicitly handling duplicates, sparse or missing DAU rows, and timezone issues. It is commonly asked in Data Manipulation (SQL/Python) interviews to assess practical data engineering and analytical skills, primarily testing practical application with an underlying need for conceptual understanding of baselining, attribution, and data-quality edge cases.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Write advanced SQL for sales support analytics

  This question evaluates practical SQL data-manipulation skills such as joins, aggregation, window functions, time-based filtering, deduplication, and computation of sales KPIs for rep-level analytics.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Write dating profile report with final reviews

  This question evaluates SQL and data-manipulation proficiency, including temporal joins, versioning, time-bounded snapshotting, windowed aggregations, and validation checks for data quality and idempotency.

  Data Manipulation (SQL/Python)Onsite
• MicrosoftMedium·Data Scientist
  Find common friends from directed edges

  This question evaluates a candidate's ability to manipulate relational data and reason about graph reciprocity, specifically testing SQL query formulation, aggregation, deduplication, and indexing for performance in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• MicrosoftEasy·Data Scientist
  Query departments and top earners

  This problem tests your ability to aggregate data using window functions to identify top-performing rows within specific groups and perform conditional filtering across joined tables.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsJoins
• MixpanelMedium·Data Scientist
  Calculate First-Week Retention by Signup Cohort

  Compute first-week activity retention by signup-week cohort with all cohort users in the denominator and zero-retention cohorts included.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Compare First-Week Retention by Plan Type

  Compare first-week retention by recorded plan type with distinct-user counts, inclusive denominators, and four-decimal rates.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Count Users Active After Signup

  Count distinct users with at least one event on or after signup using a PostgreSQL query.

  Data Manipulation (SQL/Python)Technical Screen
• MixpanelMedium·Data Scientist
  Count Users Retained on Day Seven

  Count users active on the seventh calendar day after signup, without double-counting repeated events.

  Data Manipulation (SQL/Python)Technical Screen
• MolocoMedium·Data Scientist
  Find top advertisers and cost per KPI

  This question evaluates proficiency in data manipulation and analytical SQL/Python skills, including aggregation, ranking with ties, dataset joins at a defined grain, date-based filtering relative to campaign launch dates, and handling division-by-zero scenarios when computing cost metrics.

  Data Manipulation (SQL/Python)Technical Screen
• Morgan StanleyMedium·Data Scientist
  Describe Your Analysis and Visualization Toolkit

  Tests your ability to articulate an end-to-end data analysis and visualization workflow clearly and concisely for recruiters and hiring managers, connecting technical tool choices directly to business outcomes.

  Data Manipulation (SQL/Python)HR Screendata-analysissqlpython
• NetflixMedium·Data Scientist
  Aggregate D1 retention cohorts in SQL

  This question evaluates a candidate's competency in time-series cohort analysis and SQL-based data manipulation, specifically measuring DAU, new-user cohorts, D1 retention and revenue-per-user while handling deduplication and user filtering.

  Data Manipulation (SQL/Python)Onsite