Data Scientist Interview Questions

Showing 1–30 of 339 results

• AdobeMedium·Data Scientist
  Solve SQL and Python Data Manipulation Tasks

  This question evaluates proficiency in SQL event-log aggregation and basic Python data-structure transformations, assessing competencies such as counting distinct users, identifying frequent event types, and performing dictionary/list/string manipulations.

  Data Manipulation (SQL/Python)Onsite
• 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
• AirbnbMedium·Data Scientist
  Describe How You Use SQL in Data Science Work

  This question assesses a candidate's ability to articulate their technical workflow in SQL, focusing on data extraction strategies, quality assurance, and how technical analysis translates into business impact.

  Data Manipulation (SQL/Python)HR ScreenSQLData ScienceAnalytics
• AirwallexMedium·Data Scientist
  Compute Moderation View Metrics

  An Airwallex Data Scientist onsite SQL question on content-moderation analytics. You write three queries over a per-day view table and a one-to-many post/violation-type policy-score table: distinct posts with 10+ views in the last 7 days, Nudity view prevalence over the last 30 days, and a monthly violation-type distribution. The core challenge is windowed aggregation plus avoiding view double-counting across violation types.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Analyze Top 10 Items' Revenue Contribution by Category

  This question evaluates proficiency in data manipulation and analytical querying—aggregation, window functions, ranking and revenue attribution—alongside conceptual knowledge of normalization versus denormalization and ETL pipeline design.

  Data Manipulation (SQL/Python)Onsite
• 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 panel and compute effects in SQL

  This question evaluates proficiency in panel data construction and causal inference workflows, specifically difference-in-differences estimation, SQL aggregation for user-week metrics, and propensity-score matching implemented in Python.

  Data Manipulation (SQL/Python)HR 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
  Compute daily work hours from in/out events

  This question evaluates a candidate's ability to manipulate time-series punch-event data, including temporal pairing of in/out events, splitting shifts across calendar days, rounding and aggregating hours, and detecting data-quality issues such as overlaps or orphaned events using SQL or Python.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Compute join counts and window ranks

  This question evaluates SQL data-manipulation and analytic competencies including join semantics, set operations, window functions, view versus materialized view trade-offs, key and partition design, and query-tuning techniques in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• 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
  Design student–course data models and SQL

  This question evaluates data modeling, database design, and SQL proficiency—covering normalization, schema constraints, indexing, star-schema warehousing, slowly changing dimensions, partitioning strategies, and complex analytic queries—in the domain of relational databases and data warehousing (Data Manipulation/SQL).

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Diagnose MySQL joins and GROUP BY/HAVING errors

  This question evaluates mastery of SQL join semantics (LEFT, RIGHT, FULL OUTER emulation, CROSS), NULL equality and duplicate handling, GROUP BY/HAVING behavior under ONLY_FULL_GROUP_BY, aggregation result shaping and type/format conversions in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Online Assessment
• 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 Top-3 Salaries Per Department Using SQL

  This question evaluates SQL data manipulation competency, focusing on group-wise aggregation, ordering, and selecting the top-N salary values within each department.

  Data Manipulation (SQL/Python)Onsite
• 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
  Find top-spend categories per customer with ranking

  This question evaluates proficiency with SQL aggregation and JOINs, analytic/window functions (RANK()), tie and NULL/edge-case handling, and percent-of-total calculations for per-customer spend analysis.

  Data Manipulation (SQL/Python)Onsite
• AmazonMedium·Data Scientist
  Identify First Daily Order for Each Merchant

  This question evaluates proficiency in data manipulation with SQL and related tools, focusing on identifying the first transaction per merchant for each calendar date and handling ordering and deduplication within grouped transactional records.

  Data Manipulation (SQL/Python)Onsite
• 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