Data Scientist Interview Questions

Showing 1–30 of 93 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
• 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
  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
  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
  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 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
  Transform event logs with subscription windows in pandas

  This question evaluates a candidate's competency in time-based event attribution and temporal interval manipulation using pandas, covering interval merging, subscription-aligned revenue aggregation, anomaly flagging, and handling edge cases like overlapping or back-to-back subscriptions.

  Data Manipulation (SQL/Python)Onsite
• AppleEasy·Data Scientist
  Write queries to compute salary and budget stats

  This set of tasks evaluates data manipulation and analytical competencies, specifically the ability to compute order-statistics on numeric columns, perform relational joins to enrich event records, and reason about constrained selection for maximizing hires using SQL and pandas.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Audit flight data quality from metadata

  This question evaluates data quality auditing competencies, including validation of timestamps and timezones, detection of label leakage and inconsistent units, provenance and schema enforcement, and hands‑on use of SQL/Python for dataset inspection.

  Data Manipulation (SQL/Python)Onsite
• Capital OneEasy·Data Scientist
  Clean and Merge Housing Data

  This question tests the ability to clean messy housing datasets, handle inconsistent data types, and perform efficient left-joins with external reference data.

  Data Manipulation (SQL/Python)OnsitePandasData CleaningSQL
• Capital OneMedium·Data Scientist
  Compute Customer Spend and Engineer Features for 2023

  This question evaluates proficiency in SQL-based temporal aggregation and Python feature engineering on transactional datasets, including computing monthly spend summaries and engineering category-level spending features.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Determine Country with Most 'Sunny' Days

  This question evaluates proficiency in data aggregation, grouping, and filtering to summarize categorical time-series records using SQL or equivalent Python data-manipulation tools, and it belongs to the Data Manipulation domain with an emphasis on practical application of query and aggregation techniques.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Write SQL for theme-park revenue and visits

  This question evaluates SQL data-manipulation and analytical skills, including joins, aggregations, date-range filtering, handling multiple ticket ownership, and edge cases like zero counts when computing revenue and visit metrics.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Write SQL to compute campaign net revenue

  This question evaluates SQL-based data manipulation and analytics skills, including aggregation, deduplication, date-range filtering, revenue and cost calculations, and selection logic for prioritizing additional donors.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Data Scientist
  Compute adoption, latency, and cross-region transactions

  This question evaluates proficiency in SQL-based data manipulation and analytics, including calculating cohort adoption and transaction rates, performing date arithmetic for time-to-first-transaction, identifying cross-region transactions, and computing distributional percentiles.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Data Scientist
  Write SQL: sum values ≤ each row’s value

  This question evaluates proficiency in SQL aggregation, query optimization, and handling duplicate values when computing cumulative sums across rows. It is commonly asked to assess practical ability in the Data Manipulation (SQL/Python) domain to design scalable, efficient queries and demonstrates a focus on practical application of query-design and optimization rather than purely conceptual theory.

  Data Manipulation (SQL/Python)Onsite
• ConfluentMedium·Data Scientist
  Determine Tutorial Completion Rate by Variant in 7 Days

  This question evaluates a candidate's competency in data manipulation and event-based analytics, focusing on skills such as joining user and event tables, aggregating event data, and computing cohort completion metrics by variant.

  Data Manipulation (SQL/Python)Onsite
• DoorDashHard·Data Scientist
  Calculate Order Request Metrics

  This question evaluates SQL and data-manipulation competencies for computing order-level metrics, including aggregations, joins, windowed event sequencing, timestamp/timezone handling, and NULL-aware comparisons.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Compute rolling cold-delivery rates with windows

  This question evaluates proficiency with SQL window functions, time-based joins, event deduplication and business-rule implementation for computing rolling metrics over temporal windows.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Model schema and query new-market readiness

  This question evaluates SQL-based data manipulation skills including time-series aggregation, cohort analysis, percentile/median calculations, joins between event and reference tables, and deriving operational KPIs for new-market readiness; it targets the Data Manipulation (SQL/Python) domain for a Data Scientist role.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Write complex SQL on DoorDash data

  This question evaluates proficiency in complex SQL querying and data manipulation, including joins across tables, time-based filtering, window functions, null and edge-case handling, aggregations, and integrating promotion and order-event data.

  Data Manipulation (SQL/Python)Onsite
• ExpediaMedium·Data Scientist
  Audit and onboard unfamiliar datasets safely

  This question evaluates a data scientist's competency in dataset discovery, schema and key verification, profiling for distributions/missingness/timezones/currencies, duplicate and join-inflation detection, event-sequence validation, accurate metric computation, and the design of data-quality tests/data contracts within SQL/Python workflows.

  Data Manipulation (SQL/Python)Onsite
• FMFannie MaeMedium·Data Scientist
  Understand SQL Aggregations and Joins: Key Differences Explained

  This question evaluates a candidate's competency in SQL aggregations (COUNT, SUM, AVG, MIN, MAX), join semantics (INNER, LEFT, RIGHT, FULL OUTER), set operations (UNION vs UNION ALL), window functions, view versus table trade-offs, duplicate detection and removal, query optimization techniques, and pandas DataFrame operations (merge, join, concat) within the Data Manipulation (SQL/Python) domain. It is commonly asked because it assesses both conceptual understanding (function purposes and join semantics) and practical application (writing efficient queries, deduplicating data, and translating patterns between SQL and pandas), thereby probing correctness and performance reasoning.

  Data Manipulation (SQL/Python)Onsite
• Freddie MacMedium·Data Scientist
  Compute leakage-safe rolling features in pandas

  This question evaluates proficiency in time-series feature engineering, leakage prevention, memory-efficient large-scale data processing, categorical target encoding, exponential weighting, and algorithmic complexity within the Data Manipulation (SQL/Python) domain, emphasizing practical application with pandas at scale.

  Data Manipulation (SQL/Python)Onsite
• GleanMedium·Data Scientist
  SQL: Zero-Filled DAU, Rolling 30-Day MAU, and the MAU Impact of a User-ID Rehash

  Three SQL questions on a login table: daily active users with zero-filled days, rolling 30-day MAU per date with a weekday-usage follow-up, and how a user-ID rehash inflates MAU. Tests distinct counting, calendar generation, window boundaries, query cost and reasoning about metric artifacts.

  Data Manipulation (SQL/Python)Onsite
• GoogleMedium·Data Scientist
  Calculate Top Countries' Gmail Usage and MoM Change

  This question evaluates proficiency in SQL data aggregation, time-based grouping, and analytical window functions for computing month-over-month percentage changes in event volumes.

  Data Manipulation (SQL/Python)Onsite
• 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