Data Scientist Interview Questions

Showing 871–900 of 1076 results

• StripeHard·Data Scientist
  Design a leak-free time-split model

  This question evaluates a Data Scientist's competency in time-aware predictive modeling, including label leakage prevention for late-arriving events, temporal cross-validation, feature engineering, model selection (logistic regression vs gradient-boosted trees), calibration and thresholding for costed actions.

  Machine LearningTechnical Screen
• StripeHard·Data Scientist
  Design a target‑user prediction system

  This question evaluates skills in temporal label definition and leakage prevention, time‑bounded feature engineering (recency/frequency and content affinity), uplift and incremental effect estimation, model selection under class imbalance and budgeted outreach, offline metric design, calibration, fairness constraints, and production validation and monitoring. Common in Machine Learning/Data Science interviews, it is asked to gauge both conceptual understanding of causal and validation issues and practical application skills for deploying a ranked outreach model under capacity constraints, including appropriate cross‑validation and thresholding to map predictive scores to business outcomes.

  Machine LearningTechnical Screen
• StripeMedium·Data Scientist
  Design an idempotent SQL ETL for late data

  This question evaluates practical skills in idempotent ETL design, deduplication by natural keys, handling late-arriving and out-of-order records, upsert/merge strategies, and SQL-based aggregation for daily metrics.

  Data Manipulation (SQL/Python)Technical Screen
• StripeHard·Data Scientist
  Design and power an A/B test

  This question evaluates a data scientist's competency in experimentation design, including metrics selection and guardrails, statistical power and sample-size calculation, sequential monitoring and ramp strategies, operational checks for randomization and contamination, and choosing credible quasi-experimental alternatives.

  Analytics & ExperimentationTechnical Screen
• StripeMedium·Data Scientist
  Design metrics and write SQL for a case

  This question evaluates the ability to define time‑bounded, attributable conversion metrics and to implement joins, aggregations, and diagnostic checks in SQL to measure marketing outreach impact.

  Data Manipulation (SQL/Python)Technical Screen
• StripeHard·Data Scientist
  Evaluate a new product with experimentation

  This question evaluates experimental design and causal inference skills, including defining an Overall Evaluation Criterion (OEC) and guardrail metrics, selecting an appropriate test design and ramp/power strategy, specifying quasi-experimental fallbacks, and performing metric diagnostics for a recommendation module in a commerce app.

  Analytics & ExperimentationTechnical Screen
• StripeMedium·Data Scientist
  Implement streaming per-user reservoir sampling

  This question evaluates a candidate's competency in streaming algorithms, per-user reservoir sampling, probabilistic reasoning and proof of correctness, concurrent/distributed merging, and engineering trade-offs for memory and amortized update efficiency.

  Coding & AlgorithmsTechnical Screen
• StripeEasy·Data Scientist
  Match payments to invoices by memo or amount

  This question evaluates string parsing, record linkage, and algorithmic matching skills for data reconciliation tasks, including handling messy memo text, exact amount matches, and tie-breaking by earliest due date.

  Coding & AlgorithmsTechnical Screen
• StripeMedium·Data Scientist
  Quantify impact without an A/B test

  This question tests your ability to design a quasi-experimental study to estimate causal impact when randomized controlled trials (RCTs) are unavailable, specifically focusing on observational causal inference techniques.

  Analytics & ExperimentationTechnical ScreenCausal InferenceAnalyticsDifference-in-Differences
• StripeMedium·Data Scientist
  Scope an open‑ended take‑home under constraints

  This question evaluates a candidate's competency in scoping and prioritizing a data‑science take‑home, stakeholder communication, risk identification, and trade‑off defense under strict time constraints.

  Behavioral & LeadershipTechnical Screen
• StripeMedium·Data Scientist
  Write SQL for snapshot features and labels

  This question evaluates proficiency in SQL-based feature engineering for time-series and user-event data, covering snapshotting, temporal joins, deduplication, windowed aggregations, distinct counting, and label construction in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• TTCSHard·Data Scientist
  Compare Tableau blending, joins, and filters

  This question evaluates a candidate's competency in Tableau data integration and visualization, specifically assessing understanding of joins versus blends, filter order of operations, aggregation and null-handling behavior, level-of-detail impacts, and cross-database/calculation limitations.

  Analytics & ExperimentationTechnical Screen
• TTCSMedium·Data Scientist
  Find duplicates in a list efficiently

  This question evaluates a candidate's ability to design efficient algorithms for duplicate detection, occurrence counting, and preserving first-occurrence order while reasoning about time and space complexity across in-memory, in-place, and streaming data scenarios.

  Coding & AlgorithmsTechnical Screen
• TTencentMedium·Data Scientist
  Compute per-player daily cumulative games with windows

  This question evaluates proficiency with SQL window functions, aggregation, date-aware grouping and joins for computing per-player daily counts and running totals, and it falls under Data Manipulation (SQL/Python) for Data Scientist roles, emphasizing practical application rather than purely theoretical concepts.

  Data Manipulation (SQL/Python)Technical Screen
• The Trade DeskEasy·Data Scientist
  Answer common DS behavioral screen questions

  This question evaluates communication, decision-making, project execution, technical depth in modeling and evaluation, learning agility, and stakeholder management for a Data Scientist internship within the Behavioral & Leadership category.

  Behavioral & LeadershipTechnical Screen
• The Trade DeskEasy·Data Scientist
  Design a CVR model for RTB bidding

  This Trade Desk data-science screen asks you to design an end-to-end conversion-rate (CVR) model for real-time bidding (RTB) at a DSP. It covers RTB system roles, label/attribution-window definition, bid-time feature engineering and leakage, logistic-regression vs LightGBM tradeoffs, log-loss training, class-imbalance handling with calibration, PR-AUC vs ROC-AUC evaluation, and latency-aware, safely-deployed production.

  Machine LearningTechnical Screen
• The Trade DeskEasy·Data Scientist
  Implement monotonic-array linear interpolation

  This question evaluates proficiency with piecewise-linear interpolation on strictly increasing (monotonic) arrays, sound handling of out-of-range queries (error, clamp, extrapolate, or None/NaN), and efficient interval search over sorted data with a target of O(log n). It also probes real-world judgment about when each out-of-range policy is appropriate and which non-bisect strategies exist.

  Coding & AlgorithmsTechnical Screen
• ThumbtackMedium·Data Scientist
  Estimate Two Conditional Win Probabilities by Simulation

  This question tests practical coding ability in probabilistic simulation, requiring implementation of a Monte Carlo method to estimate conditional win probabilities under two decision strategies. It assesses a data scientist's skill in translating a classic probability scenario into working randomized code rather than relying on closed-form analysis.

  Coding & AlgorithmsTechnical Screen
• TikTokMedium·Data Scientist
  Analyze DFS, BFS, and A* trade-offs

  This question evaluates understanding of graph search algorithms, heuristic properties (admissibility and consistency), path optimality and time/space complexity, requiring application of BFS, uniform-cost search, A*, and DFS on a concrete weighted graph.

  Coding & AlgorithmsTechnical Screen
• TikTokMedium·Data Scientist
  Analyze Negative Reviews' Impact on Coupon Repurchase Rate

  Evaluates causal analysis of negative reviews on coupon repurchase behavior using exposure-aware data. Strong answers define treatment and outcome, collect exposure data, control confounding, and validate estimates with robustness checks.

  Analytics & ExperimentationTechnical Screen
• TikTokMedium·Data Scientist
  Calculate Conversion Rate from Ad Clicks to Page Visits

  This question evaluates a candidate's competency in event-level data manipulation and metric computation using SQL or Python, including time-based aggregation, deduplication, and joining or windowing of event streams.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Calculate Day-7 Retention Rate from User Post Data

  This question evaluates a candidate's competency in SQL and Python data wrangling, emphasizing date arithmetic, window functions, grouping, and conditional aggregation for cohort and retention calculations.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokEasy·Data Scientist
  Calculate Expected Draws for X > 0.8 in Uniform(0,1)

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Draws for X > 0.8 in Uniform(0,1) states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• TikTokMedium·Data Scientist
  Calculate User Registration Date and 7-Day Retention Rate

  This question evaluates the ability to perform time-based data manipulation and cohort retention analysis using SQL/Python, including deriving registration dates, aggregating posts within time windows, and computing retention metrics.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Calculate valid daily usage with gap constraints

  This question evaluates a candidate's ability to manipulate temporal intervals, including clipping to a specified day window, merging touching or overlapping segments, detecting gaps, and aggregating minute-level durations using SQL window functions and date/time arithmetic, and belongs to the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Compare Random Forests and Boosted Trees: Bias, Variance, Speed

  Evaluates practical trade-offs between Random Forests and gradient-boosted trees for tabular ML. Strong answers compare bias, variance, speed, interpretability, overfitting, production fit, and feature scaling needs.

  Machine LearningTechnical Screen
• TikTokMedium·Data Scientist
  Compute 7-day rolling complaint/order ratio in SQL

  This question evaluates SQL data manipulation skills, specifically time-based aggregations, sliding window functions, joins to include entities with no events, and numeric casting within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Scientist
  Compute and rank top bad advertisers

  This question evaluates proficiency in SQL data manipulation and analytics, including time-window filtering, JOINs, aggregation and deduplication, rate calculations, and window-function–based ranking.

  Data Manipulation (SQL/Python)Technical Screen
• 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
  Derive L1 vs L2 effects with correlation

  This question evaluates understanding of regularization methods (ridge/L2, LASSO/L1, and elastic net), multicollinearity effects, geometric intuition of constraint sets, and hyperparameter influence on coefficient patterns in the Statistics & Math domain.

  Statistics & MathTechnical Screen