Data Scientist Interview Questions

Showing 301–330 of 339 results

• TikTokMedium·Data Scientist
  Compute last-to-previous ad impression gaps

  This question evaluates proficiency in data manipulation and time-based analytics, covering SQL window functions, timestamp arithmetic, and grouping/ordering semantics within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Count buggy vs non-buggy by employer

  This question evaluates proficiency in SQL data manipulation—particularly conditional aggregation and join semantics—for counting categorized records across related tables in a data science context.

  Data Manipulation (SQL/Python)Online Assessment
• TikTokEasy·Data Scientist
  Find top-paid employee per department

  This question evaluates a candidate's competency in SQL data manipulation, including aggregation, ranking, handling of duplicate and historical rows, tie semantics, and temporal data interpretation when determining the top-paid employee per department.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Identify Repeat Advertisers from Consecutive Weeks

  This question evaluates a candidate's proficiency in manipulating relational data to identify recurring entities across consecutive time periods, focusing on SQL/Python data manipulation and temporal comparison of records.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Investigating Correlation Between Ad Visits and Reports

  This question evaluates a data scientist's competency in operational analytics, including defining and aggregating metrics in SQL and applying causal reasoning to interpret correlations between ad exposures and user reports.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  SQL Queries and Analysis on Bad Advertisers

  This question evaluates SQL query writing, metric definition and normalization, ranking/aggregation techniques, and analytical reasoning for identifying problematic advertisers.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Select max-discount product per category

  This question evaluates proficiency in data manipulation and SQL querying—specifically use of window functions versus set-based alternatives, group-level ranking and tie-breaking, NULL-aware expressions, and numeric precision when computing discounts within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Online Assessment
• TikTokMedium·Data Scientist
  Write SQL for 7-day geo-localized revenue dashboard

  This question evaluates SQL skills in time-zone aware local-date aggregation, idempotent event deduplication, cohorting for new versus returning users, calculation of revenue and conversion metrics, and anomaly detection via median-based drop flags.

  Data Manipulation (SQL/Python)HR Screen
• TikTokEasy·Data Scientist
  Write SQL for TikTok Live creator metrics

  This question evaluates SQL data-manipulation competencies such as joins, aggregations, group-by and window functions, handling missing activity rows, date-range filtering, country partitioning, and deterministic tie-breaking for top-k metrics.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Write SQL for geo posting-frequency drops

  This question evaluates proficiency in SQL-based time-series data manipulation, including time zone–aware date normalization, window functions, aggregation, filtering and median computations within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• TikTokMedium·Data Scientist
  Write SQL for last-7-day metrics without windows

  This question evaluates proficiency in ANSI SQL data manipulation, including aggregation, de-duplication, date arithmetic, joins and subqueries, GROUP BY/HAVING usage, and cohort/retention and conversion calculations performed without window functions.

  Data Manipulation (SQL/Python)Technical Screen
• TFTools For HumanityEasy·Data Scientist
  Compute reply-based engagement metrics

  This question evaluates proficiency in data manipulation and analytics, emphasizing SQL/Python skills for temporal filtering, joins between posts and users, deduplication, aggregation (distinct counts), and percentage calculations used to compute reply-based engagement metrics.

  Data Manipulation (SQL/Python)Technical Screen
• TubiMedium·Data Scientist
  Compute daily net users from event logs

  This question evaluates SQL-based data manipulation skills focused on daily aggregation, cohort and retention calculations, deduplicating event records, and grouping metrics by device_platform.

  Data Manipulation (SQL/Python)Technical Screen
• TwitchMedium·Data Scientist
  Analyze Streaming Data with SQL for Monthly Trends

  This question evaluates proficiency in SQL time-series aggregation, date extraction and grouping across calendar months, join and conditional aggregation techniques, windowed month-over-month comparisons, and handling sparse or multi-year datasets; Category: Data Manipulation (SQL/Python); level: practical application.

  Data Manipulation (SQL/Python)Technical Screen
• TwitchMedium·Data Scientist
  Solve advanced SQL for streaming analytics

  This question evaluates advanced SQL competency for streaming analytics — covering time-series aggregation, monthly bucketing across year boundaries, joins between event tables, window functions, and case-insensitive substring filtering; the domain is Data Manipulation (SQL/Python) and the level is practical application of SQL techniques.

  Data Manipulation (SQL/Python)Technical Screen
• Two SigmaMedium·Data Scientist
  Analyze NYC taxi trips efficiently over last 7 days

  This question evaluates proficiency in time-series filtering, relational joins, grouped aggregations (median and high-percentile calculations), numeric stability of percentile methods, and performance-aware vectorized data manipulation using SQL or pandas.

  Data Manipulation (SQL/Python)Online Assessment
• UberMedium·Data Scientist
  Compute ETA shift and conversion uplift

  This question evaluates proficiency in SQL and Python for time‑series data manipulation and experimental analysis, covering window functions (7‑day percentile_cont medians), date spines and timezone‑aware timestamp truncation, conversion-rate aggregation, difference‑in‑differences uplift estimation, and CUPED adjustment with cluster‑robust standard errors. It is commonly asked to assess practical data engineering and applied statistics skills in the Data Manipulation (SQL/Python) domain, testing applied implementation and causal inference ability rather than only conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Data Scientist
  Write SQL and Pandas for Uber Trips

  This question evaluates SQL and Pandas data manipulation competencies, including joins, time-based filtering, handling NULLs, aggregations, rate and ratio calculations, and windowed comparisons over grouped city-day data.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Data Scientist
  Write SQL/Python for CTR analytics

  This question evaluates proficiency in data manipulation and analytics, covering SQL windowing and deduplication for time‑windowed cohort metrics (48‑hour unique CTR) as well as practical Python model evaluation tasks such as precision‑recall plotting, weighted F1 threshold selection, and precision@top‑k.

  Data Manipulation (SQL/Python)Technical Screen
• UpstartMedium·Data Scientist
  Calculate Average Event Value by User ID

  This question evaluates a candidate's skill in group-wise aggregation and data transformation on JSON-like event streams, specifically computing per-user average metrics and producing ranked results.

  Data Manipulation (SQL/Python)Technical Screen
• UpstartMedium·Data Scientist
  Calculate User Revenue and Session Duration in Python

  This question evaluates proficiency in data manipulation and time-series event processing in Python, focusing on per-user revenue aggregation and sessionization to compute average session duration.

  Data Manipulation (SQL/Python)Technical Screen
• VoleonEasy·Data Scientist
  Analyze a Regression Dataset and Build a Safe File-Processing Utility

  Implement an atomic, non-destructive file-cleaning utility for tabular logs and a greedy collinearity filter to screen regression features based on target correlation.

  Data Manipulation (SQL/Python)Technical ScreenPythonPandasFeature Engineering
• VoleonMedium·Data Scientist
  Analyze time-zoned events with pandas

  This question evaluates proficiency in data manipulation and analysis with pandas and SQL, covering timezone-aware timestamp handling, event deduplication, cohort and retention metrics, currency parsing and FX conversion, revenue imputation, ARPU calculation, and scalability for high-volume event data.

  Data Manipulation (SQL/Python)Technical Screen
• VoleonMedium·Data Scientist
  Load and visualize large CSV robustly

  This question evaluates competency in large-scale data manipulation and preprocessing, including robust file I/O (delimiter and encoding detection), memory-efficient chunked loading, dtype enforcement, deduplication, imputation strategy design, outlier handling, and statistical visualization techniques.

  Data Manipulation (SQL/Python)Technical Screen
• VoleonMedium·Data Scientist
  Pre-process Financial Data for Linear Regression Modeling

  This question evaluates data preprocessing and feature engineering skills—handling missing values, winsorizing outliers, standardizing predictors, and assembling X/y datasets using Python/Pandas within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• WayfairEasy·Data Scientist
  Clean scores and return top 5 students

  This problem tests your ability to clean tabular data by performing conditional imputation and applying window functions to rank performance metrics.

  Data Manipulation (SQL/Python)Online AssessmentPandasData CleaningImputation
• WayfairEasy·Data Scientist
  Find top buyer in earliest 10-year window

  This question tests the ability to filter data by date ranges, group transactional records by user, and use window functions or aggregations to identify a specific record based on criteria.

  Data Manipulation (SQL/Python)Online AssessmentSQLAggregationWindow Functions
• WayfairEasy·Data Scientist
  Solve SQL and Python OA tasks

  This question evaluates SQL and pandas data-manipulation skills, including joined aggregations over date windows, handling of missing values with median imputation, and multi-criteria sorting and tie-breaking.

  Data Manipulation (SQL/Python)Online Assessment
• WaymoMedium·Data Scientist
  Analyze User Ride Activity with SQL

  Solve four SQL analytics tasks on ride and user tables: inactive users, monthly rides and ratings, low-frequency users by city, and first-versus-third active-month ratings. The walkthrough emphasizes outer joins, date boundaries, aggregation grain, window functions, and weighted averages.

  Data Manipulation (SQL/Python)Online Assessment
• WaymoMedium·Data Scientist
  Compute Ride Metrics in SQL

  This question evaluates SQL data manipulation skills, covering joins between normalized tables, timezone-aware date filtering and calendar-month grouping, aggregation and NULL-aware averages, counting distinct and inactive users, and cohort/retention analysis.

  Data Manipulation (SQL/Python)Online Assessment