Data Scientist Interview Questions

Showing 1651–1680 of 1971 results

• StripeMedium·Data Scientist
  Evaluate Stripe Capital Lending Strategy

  This question evaluates data science competencies in product analytics, credit risk modeling, monitoring and instrumentation for a merchant lending product, including dashboard metric design, early-warning signal engineering, offer-structuring trade-offs, and portfolio-level profit attribution.

  Analytics & ExperimentationOnsite
• 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
  How Should Stripe Capital Be Evaluated?

  This problem tests your ability to design a performance measurement framework for a lending product, focusing on balancing merchant growth with credit risk.

  Analytics & ExperimentationOnsiteCredit RiskProduct AnalyticsCohort Analysis
• 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
  Prioritize a 6-hour take-home effectively

  This question evaluates a candidate's ability to scope and time-box work, prioritize analyses and features, articulate trade-offs and risks, and produce concise deliverables under a tight deadline, reflecting judgment and leadership competencies for a Data Scientist.

  Behavioral & LeadershipOnline Assessment
• 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
  Resolve conflicts and prioritize with stakeholders

  This question evaluates stakeholder management, prioritization frameworks, cross-functional conflict resolution, and decision documentation skills within a payments analytics context for a Data Scientist role.

  Behavioral & LeadershipOnsite
• 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
• StripeMedium·Data Scientist
  Write SQL to detect recurring non-subscription users

  This question evaluates a candidate's ability to perform advanced SQL data manipulation and temporal pattern detection in transactional datasets, including attribute-based matching (amount and card fingerprint) and filtering by transaction status.

  Data Manipulation (SQL/Python)Online Assessment
• StripeMedium·Data Scientist
  Write SQL to monitor weekly chargeback spikes

  This question evaluates proficiency in PostgreSQL time-series analytics, specifically aggregations, joins across payments and chargebacks, handling late-arriving events, and calculating per-week metrics such as GMV, active merchants, and chargeback rates.

  Data Manipulation (SQL/Python)Onsite
• 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
  Compare list/dict; parse JSON/CSV at scale

  This question evaluates understanding of Python data structures (list vs dict), algorithmic time and memory complexity, ordering guarantees in CPython, large-scale parsing and streaming of JSON/CSV, and robust data-cleaning and error-handling strategies.

  Data Manipulation (SQL/Python)Onsite
• ThumbtackMedium·Data Scientist
  Compute weekly 3-week rolling sums in SQL

  This question evaluates time zone–aware date bucketing, gapless calendar generation, and rolling-window aggregation skills, demonstrating competency in SQL-based data manipulation and temporal reasoning.

  Data Manipulation (SQL/Python)HR Screen
• ThumbtackMedium·Data Scientist
  Compute weighted response rates by job category

  This question evaluates a candidate's data manipulation and statistical estimation skills, including pandas-based aggregation, invitation-weighted rate calculation, confidence-interval computation, and robust handling of data-quality issues, in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• ThumbtackHard·Data Scientist
  Design a robust pro-ranking A/B test

  It evaluates experimental design and causal inference competencies for two-sided marketplaces, focusing on metric definition and guardrails, randomization to limit interference, power and sample-size estimation, bias controls, monitoring and sequential stopping, and attribution of uplift versus cannibalization.

  Analytics & ExperimentationOnsite
• 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
• ThumbtackMedium·Data Scientist
  Implement min, mean, median robustly

  This question evaluates implementation skills in basic descriptive statistics (min, mean, median), numerical stability in online aggregation methods, robust handling of edge cases like None/NaN and extreme numeric ranges, and algorithmic complexity reasoning in the Coding & Algorithms domain for a Data Scientist role.

  Coding & AlgorithmsOnsite
• ThumbtackHard·Data Scientist
  Lead XFN decision under tight timeline

  This question evaluates cross-functional leadership, stakeholder alignment, rapid decision-making under time pressure, data triage and technical judgment, executive communication, and risk management skills.

  Behavioral & LeadershipOnsite
• ThumbtackMedium·Data Scientist
  Test regional response-rate differences rigorously

  This question evaluates applied statistical modeling and causal-inference competencies, including adjustment for confounding, analysis of proportion/count outcomes, variance estimation under clustering, multiple-comparison control, diagnostic detection of Simpson’s paradox, and clear effect-size reporting; it is categorized under Statistics & Math and emphasizes practical application with necessary conceptual understanding. Such problems are commonly asked to assess the ability to justify and interpret comparative analyses that account for covariate mix, temporal structure, and multiple-testing concerns, and this summary is in English.

  Statistics & MathOnsite
• TikTokHard·Data Scientist
  Act when A/B result is not significant

  This question evaluates a candidate's competency in experimental design and statistical decision-making, covering sample size calculation, variance-reduction methods, interpretation of non-significant A/B results, multiplicity control, and sequential/Bayesian decision frameworks.

  Statistics & MathOnsite
• 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
  Analyze promo anomaly and design risk guardrails

  This question evaluates a candidate's ability to diagnose promotional anomalies, distinguish coordinated abuse from organic virality, design real-time approve/hold/reject decisioning with cost trade-offs, and specify experiment and monitoring plans, testing competencies in fraud analytics, operational risk modeling, and experimentation.

  Analytics & ExperimentationHR Screen