Data Scientist Interview Questions

Showing 1–30 of 205 results

• AffirmMedium·Data Scientist
  Analyze Loan Payments Using Pandas for Key Insights

  This question evaluates data manipulation and aggregation skills using pandas, focusing on computing per-loan payment totals, payment success rates, and a repayment-status flag. It is commonly asked to assess applied data wrangling and numeric summarization abilities in the Data Manipulation (SQL/Python) domain, testing practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Aggregate User Activity, Fit Regression, Interpret Coefficients

  This question evaluates skills in relational data aggregation and feature engineering using SQL alongside fitting and interpreting regression models in Python, targeting competencies in transforming event-level user metrics into a modeling dataset and interpreting coefficient estimates.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Build panel in SQL; run causal regression

  This question evaluates proficiency in SQL-based panel data construction, time-series aggregation and rolling-window metrics together with applied causal inference in Python, including two-way fixed-effects difference-in-differences, clustered standard errors, lead tests, and count-model re-specification; it targets Data Manipulation (SQL/Python) skills relevant for Data Scientist roles. It is commonly asked because it combines practical implementation and conceptual understanding—testing temporal joins, treatment timing, avoidance of lookahead/immortal-time biases and late-adopter issues, interpretation of interaction coefficients and confidence intervals, and the ability to reason about identification and model choice.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Compute C/T metrics from bookings and visits

  This question evaluates time-window attribution, visit deduplication, control vs treatment metric computation, aggregation of conversions and revenue, and statistical interval estimation (Wilson CIs), testing practical data manipulation skills with SQL or Python in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Compute browsing metrics in Python from logs

  This question evaluates a candidate's ability to perform event-level data manipulation and analytics in Python and SQL, covering sessionization, time-zone–consistent timestamp handling, deduplication, funnel construction, segment-level aggregation, conversion-rate computation, and statistical confidence intervals such as the Wilson CI.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Append country tables and rank salaries in USD

  This question evaluates skills in data manipulation and integration, specifically appending country-level tables, normalizing salaries via historical exchange rates, deduplicating by a composite primary key, and ranking results, with implementations expected in both SQL and pandas.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Build DiD dataset with SQL

  This question evaluates proficiency in SQL-based data manipulation and panel construction for staggered-adoption difference-in-differences, testing skills in handling adoption dates, event-time calculations, boundary conditions, and aggregation in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Defect Rate and Identify Top Lanes for Carriers

  This question evaluates competency in SQL-based data manipulation for a data scientist role, covering joins, aggregations, date-based filtering, metric computation, and use of window functions to derive week-over-week changes.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Monthly Revenue from Orders in 2023

  This question evaluates proficiency in SQL-based data manipulation, including aggregation, date extraction, and combining transactional tables to produce business metrics, and falls under the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Weekly, Monthly Hours Watched by Premium Users

  This question evaluates a candidate's ability to perform time-based aggregation and data manipulation in SQL and Python, including grouping, filtering by subscription status, and converting minute-level metrics into hours.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Compute a Rolling Seven-Day Revenue Sum with Missing Dates

  Tests the ability to write a PostgreSQL query that generates a continuous daily time series per seller, handles missing sales dates, filters by order status, and computes a rolling 7-day revenue sum.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions
• AmazonMedium·Data Scientist
  Consolidate and Rank Global Salaries in USD

  This question evaluates data consolidation, currency normalization, and ranking competencies, measuring the ability to combine country-specific employee data and convert salaries into a common USD basis within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Create Country-Level Spend Report Using Pandas

  This question evaluates proficiency in data manipulation using pandas, including relational joins and aggregation to produce country-level summary statistics like totals and averages.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Create SQL Queries for Sales and Customer Analysis

  This question evaluates proficiency in SQL-based data manipulation, covering aggregation and dispersion metrics, null handling, relational joins, and window functions for ranking customers.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Design Incremental Load Process for Large Relational Table

  This question evaluates understanding of incremental loading, change-data-capture, idempotent upsert logic, deduplication, partitioning, and data quality controls for large relational tables within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Explore Subscription Patterns and Status Transitions with SQL/Pandas

  This question evaluates proficiency in time-series and event-sequence data manipulation, temporal aggregation, and data quality validation using SQL and pandas, focusing on identifying status transitions and date-based uniqueness.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Find recommended friend pairs by shared songs

  This question evaluates data manipulation skills—specifically SQL joins and anti-joins, deduplication, date/time handling, aggregation, and pairwise overlap computation—within the Data Manipulation (SQL/Python) domain at a practical implementation level requiring query-writing to produce per-day overlap counts.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  List Top Customers and Monthly Order Counts in SQL

  This question evaluates a candidate's ability to perform SQL aggregation, grouping, top‑N selection, and date-based summarization to compute customer spend and chronological monthly order counts.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Select Top Customers Using Transaction Data Filters

  This question evaluates a candidate's ability to perform data aggregation, filtering, ranking, and deterministic tie-breaking on transactional datasets, assessing skills in Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Understand SQL: DELETE vs TRUNCATE, VIEW vs TABLE, CROSS JOIN

  This question evaluates a candidate's understanding of core SQL concepts and data-manipulation competencies, covering the behavior of data-deletion operations, distinctions between logical and physical data structures, and join semantics.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Verify subscriptions and analyze orders with SQL/Python

  This question evaluates proficiency in SQL and pandas data manipulation, covering data-quality validation, temporal sequence reasoning, deduplication, deterministic aggregation, and tie-breaking logic.

  Data Manipulation (SQL/Python)Technical Screen
• AtlassianMedium·Data Scientist
  Find 2023 NCAA championship winner

  This question evaluates proficiency in SQL data manipulation and relational reasoning, testing the ability to identify a championship winner from game result records across related tables in the Data Manipulation (SQL/Python) domain for data scientist roles.

  Data Manipulation (SQL/Python)Technical Screen
• B(Block (Square)Medium·Data Scientist
  Write left-join queries with tricky filters

  This question evaluates SQL data-manipulation skills including LEFT JOIN semantics, aggregation functions like COUNT(DISTINCT) and SUM, GROUP BY/HAVING logic, and date-window filtering for referral and order analytics.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Manipulate and merge DataFrames correctly

  This question evaluates proficiency in pandas-based data manipulation and merging, covering join validation, vectorized revenue and discount calculations, missing-value imputation, groupby/transform usage, dtype setting, and avoiding SettingWithCopy issues.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Query top spenders and 7-day growth

  This question evaluates proficiency in SQL data manipulation, including aggregations, date-range filtering, joins, window functions for ranking and tie-breaking, and computing revenue shares and growth rates within a relational schema.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Transform messy transactions with pandas

  This question evaluates proficiency in large-scale data manipulation and feature engineering using pandas, covering timestamp normalization across time zones, deduplication and last-write-wins semantics, data validation, memory-efficient aggregation, rolling-window statistics, and top-k categorical extraction for merchant categories.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Visualize and Clean SKU Sales Data for Outliers

  This question evaluates data manipulation and exploratory data analysis skills, including aggregation, outlier detection (IQR/Z-score) and visualization using Python libraries such as pandas and matplotlib/seaborn.

  Data Manipulation (SQL/Python)Technical Screen
• BByteDanceMedium·Data Scientist
  Campaign Efficiency Metrics and Below-Average CTR

  Practice a data scientist SQL interview question that calculates campaign cost and funnel rates, handles zero denominators, and filters campaigns against an average CTR benchmark. The exercise tests PostgreSQL date arithmetic, CTE organization, NULL-safe calculations, and deterministic output.

  Data Manipulation (SQL/Python)Technical Screen
• BByteDanceMedium·Data Scientist
  Compare SQL counts, windows, and NULL semantics

  This question evaluates understanding of SQL aggregation and NULL semantics, window functions (ROW_NUMBER vs RANK), join behavior to avoid double-counting, and defensive handling of NULLs and divide-by-zero using COALESCE and NULLIF in a data manipulation context.

  Data Manipulation (SQL/Python)Technical Screen
• BByteDanceMedium·Data Scientist
  Find Each User's Earliest Seven-Day Activity Streak

  Tests your ability to identify consecutive date sequences (gaps-and-islands problem) in PostgreSQL, aggregate streaks, and filter for each user's earliest qualifying period.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions