Data Scientist Interview Questions

Showing 121–150 of 339 results

• IBMEasy·Data Scientist
  Write SQL to compute max and min marks

  This question evaluates a candidate's ability to perform basic data aggregation in SQL, specifically computing summary statistics from a single table. It is commonly asked to assess competence in data manipulation and query result shaping within the Data Manipulation (SQL/Python) domain, focusing on practical application of SQL aggregation rather than purely conceptual theory.

  Data Manipulation (SQL/Python)Online Assessment
• InstacartMedium·Data Scientist
  Aggregate weekly revenue and attribute 4% drop

  This question evaluates a candidate's ability to perform time-based revenue aggregation, filtering, percent-change calculation, and geo-level attribution using SQL or Python, with attention to ISO-week alignment and revenue computation.

  Data Manipulation (SQL/Python)Technical Screen
• InstacartMedium·Data Scientist
  Calculate Weekly Revenue and Order Count for Standard Deliveries

  This question evaluates proficiency in time-based aggregations and filtering on order-level transactional data to compute weekly revenue and order counts specifically for standard delivery orders.

  Data Manipulation (SQL/Python)Technical Screen
• InstacartMedium·Data Scientist
  Explain handling very large datasets

  This question evaluates a candidate's competence in ingesting and processing very large datasets, covering storage formats and partitioning, memory and compute constraints, schema evolution, data quality checks, indexing strategies, tool selection in SQL/Python ecosystems, and code-level performance optimizations.

  Data Manipulation (SQL/Python)HR Screen
• InstacartMedium·Data Scientist
  Write SQL to rank advertisers and profitability

  This question evaluates a candidate's ability to perform ANSI SQL data transformations including unpivoting denormalized tables, aggregations, joins, and financial metric calculations such as net profit and profit margin while handling nulls and tie-breaking rules.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitMedium·Data Scientist
  Compute churn and revenue churn in SQL

  This question evaluates a candidate's ability to compute subscription churn and revenue retention metrics from monthly snapshots, exercising SQL data-manipulation competencies such as handling missing or null MRR values, deduplicating snapshot loads, joining across months, and aggregating MRR changes.

  Data Manipulation (SQL/Python)HR Screen
• IntuitMedium·Data Scientist
  Compute monthly new subscribers and YoY deltas

  This question evaluates proficiency in SQL time-series aggregation and cohort identification, including generating a month spine, computing year-over-year deltas, handling nulls and division-by-zero, and identifying partial months via a data watermark.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitMedium·Data Scientist
  Design an idempotent churn ETL pipeline

  This question evaluates a candidate's competence in designing robust ETL pipelines, covering idempotent processing, handling late-arriving streaming subscription events, SCD Type 2 slowly changing dimensions, incremental backfills, data quality checks, upsert/merge semantics, and metric versioning.

  Data Manipulation (SQL/Python)HR Screen
• IntuitMedium·Data Scientist
  Exclude free subscribers via anti-join

  This question evaluates competency with SQL anti-joins, join semantics, deduplication, and correct pre-aggregation filtering to prevent excluded entities from affecting aggregated monthly and year-over-year metrics; it is categorized as Data Manipulation (SQL/Python) for a Data Scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitEasy·Data Scientist
  Pivot daily users and revenue by platform

  This question evaluates data manipulation and aggregation skills, specifically computing distinct user counts and revenue aggregates and reshaping results by platform; it is in the Data Manipulation (SQL/Python) domain and assesses practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• LLendingClubMedium·Data Scientist
  Query Active Loans by Borrower ID in SQL

  This question evaluates proficiency with SQL data aggregation, grouping, and ordering to compute borrower-level loan totals, assessing practical skills in relational data manipulation within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInEasy·Data Scientist
  Analyze member video posting behavior by country

  A LinkedIn data scientist technical-screen question on member video-posting behavior using two relational tables. It tests joins, first-event derivation with MIN/GROUP BY, conditional grouping for US vs non-US comparison, and distinct-member counts per country with a length filter.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInEasy·Data Scientist
  Compute each member’s current notification status

  A LinkedIn data scientist SQL screen: given an end-of-January notification-status snapshot and a February log of turn_on/turn_off actions, compute each member's current status as of 2020-02-29, keeping members from either table. Follow-ups explore reconstructing state when the action log has no date column — unsolvable in general, but recoverable via action-count parity when every action is a guaranteed valid transition.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  Count Article Types Viewed

  Count Article Types Viewed 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)Technical Screen
• LinkedInMedium·Data Scientist
  Device Status without Timestamps

  This question evaluates data manipulation and behavioral analytics competencies, emphasizing aggregation, deduplication, distinct counts, and date-based filtering to measure author and viewer activity.

  Data Manipulation (SQL/Python)Technical Screen
• LinkedInMedium·Data Scientist
  [SQL] Job Ad Metrics with Applicant Filter

  This question evaluates proficiency in SQL data manipulation and deduplication, focusing on aggregations, distinct counts, and conditional filtering of event records (views vs applies).

  Data Manipulation (SQL/Python)Technical Screen
• LyftMedium·Data Scientist
  Query and transform marketplace data in SQL/Python

  This question evaluates mastery of data manipulation and analytics skills in the Data Manipulation (SQL/Python) domain, including SQL aggregation and window functions, time-based joins and grouping, deduplication of status streams, handling late-arriving events, and implementing rolling metrics using both SQL and pandas.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Analyze Bookstore Data to Identify Top Payment Methods

  This question evaluates data manipulation and analytical SQL/Python competency, focusing on relational joins, aggregations, string filtering, ranking, and proportion calculations across multiple tables in a bookstore schema.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Hashtag Follow Behavior with SQL Queries

  This question evaluates a candidate's competency in data manipulation and analytical SQL techniques—specifically aggregation, filtering, joins, and calculation of proportions—to analyze hashtag follow behavior.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Analyze Mobile Promo Orders with SQL Query and Metrics

  This question evaluates SQL data-manipulation and analytical competencies, including filtered joins, conditional aggregation, date-window filtering, and business-metric design to measure promotional performance on mobile orders.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Oculus App Engagement with SQL Queries

  This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Analyze Seller Activity and Vehicle Listing Interactions

  This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze Seller Activity and Vehicle Listing Interactions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Social Media Engagement with SQL Queries

  This question evaluates SQL data-manipulation and analytics competencies, including aggregation, DISTINCT counting, joins between view and action logs, date-range filtering, and conditional metric computation from event data.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze Thirty-Day Ad Performance with SQL

  This problem tests proficiency in window functions, time-based aggregation, and join logic to analyze multi-dimensional advertising performance metrics.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationTime-Series
• MetaMedium·Data Scientist
  Analyze Top Call Initiators and Active French Video Callers

  This question evaluates proficiency in SQL-based data manipulation and analytics—specifically aggregation, grouping, time-window filtering, and percentage calculations—by asking for the top call initiators over the last seven days and the proportion of active users in France on video calls yesterday, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role with a focus on practical application. It is commonly asked in technical interviews to assess a data scientist's ability to extract actionable engagement metrics from event logs, reason about temporal windows and population denominators, and produce accurate, explainable reporting from production-style datasets.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Analyze User Engagement and Spammer Read-Rate in SQL

  This question evaluates SQL-based data manipulation and analytics skills—specifically joins, time-window filtering, aggregation, and metric calculation for measuring user engagement and spammer read-rate—within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Scientist
  Calculate Ad Performance with Click-Through and Conversion Rates

  This question evaluates a candidate's competency in designing and computing ad-performance KPIs by performing data manipulation, joining event tables, applying time-window filters, and aggregating metrics using SQL or Python.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Calculate CTR and ad revenue

  This problem tests your ability to clean and join datasets, handle currency conversion, and aggregate metrics at different granularities.

  Data Manipulation (SQL/Python)Technical ScreenSQLData AggregationMetrics
• MetaMedium·Data Scientist
  Calculate Daily Survey Response Rates by Country

  Write a PostgreSQL query to compute daily survey response rates at the country level using distinct user counts while handling orphan response logs and edge cases.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLData Aggregation
• MetaMedium·Data Scientist
  Calculate French User Engagement and U.S. Call Duration

  This question evaluates proficiency in SQL-based data manipulation and analytics, testing skills such as joining event and profile tables, aggregating metrics, and computing user-level engagement proportions and average durations.

  Data Manipulation (SQL/Python)Technical Screen