Data Scientist Interview Questions

Showing 811–840 of 1076 results

• RobloxMedium·Data Scientist
  Implement streaming CTR with deduplication

  This question evaluates streaming data-processing and algorithmic design skills, specifically time-windowed aggregation, click deduplication, late-event handling, state management, and selection of data structures for per-campaign CTR computation.

  Coding & AlgorithmsTechnical Screen
• RobloxMedium·Data Scientist
  Measure Success for a New Chat System

  Build a chat-system launch framework with meaningful communication metrics, reliability and safety guardrails, and experiments that address network effects.

  Analytics & ExperimentationTechnical Screen
• RobloxMedium·Data Scientist
  Write SQL for ads metrics and variability

  This question evaluates a candidate's ability to perform SQL-based data manipulation and analytics, including deduplication of events, computation of daily and campaign-level metrics (CTR, CPC), and calculation of statistical summaries (mean and standard deviation) and outlier detection over a specified date range.

  Data Manipulation (SQL/Python)Technical Screen
• RobloxEasy·Data Scientist
  Write SQL for influence score and follower growth

  This question evaluates SQL data manipulation competencies such as joins, aggregations, distinct counts, window functions, and time-based grouping for computing an influence score (engagements on a user's posts plus follower count) and monthly follower growth, and it falls under Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen
• RRokuEasy·Data Scientist
  Explain Bias-Variance Trade-off Simply for Stakeholders

  Evaluates the ability to explain the bias-variance trade-off to non-technical stakeholders. Strong answers define bias as underfitting, variance as overfitting, use a clear analogy, and explain why models must generalize to new data rather than memorize training data.

  Statistics & MathTechnical Screen
• RRokuMedium·Data Scientist
  Explain and quantify bias-variance tradeoff

  This question tests the conceptual understanding of the bias-variance decomposition of mean squared error and how specific modeling choices influence these components.

  Machine LearningTechnical ScreenBias-VarianceModel EvaluationSupervised Learning
• RRokuMedium·Data Scientist
  Measure Speaker's Impact Using Propensity Score Matching

  Evaluates causal inference for optional smart speaker ownership using propensity score matching and staggered adoption. Strong answers address self-selection, construct matched or weighted controls, validate balance and pre-trends, estimate ATT, and run placebo and sensitivity analyses.

  Analytics & ExperimentationTechnical Screen
• RRokuMedium·Data Scientist
  Write SQL for profit, growth, retention

  This question evaluates a candidate's ability to perform advanced SQL data manipulation — specifically rolling-window aggregations, revenue and gross profit computations that account for refunds, order status and test flags, plus cohort retention and week-over-week growth analysis.

  Data Manipulation (SQL/Python)Technical Screen
• SIG (Susquehanna)Medium·Data Scientist
  Analyze Knights-and-Knaves Dialogue Variants

  Analyze Knights-and-Knaves Dialogue Variants evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• SIG (Susquehanna)Hard·Data Scientist
  Compute answers to probability and counting puzzles

  This problem tests your ability to model sequential probabilistic events using combinatorics and conditional probability, specifically requiring the calculation of expected outcomes in a game of chance.

  Statistics & MathTechnical ScreenProbabilityExpectationCombinatorics
• SIG (Susquehanna)Easy·Data Scientist
  Determine knights and knaves roles

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine knights and knaves roles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Statistics & MathTechnical Screen
• ShopifyMedium·Data Scientist
  Analyze Pirate Theme Usage Growth Over Time

  This question evaluates a candidate's ability to perform SQL-based data manipulation and time-series analysis, emphasizing joins, aggregation, time-bucketing, and definition of growth metrics to measure feature adoption.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Analyze Pirated Theme Usage

  Calculate monthly metrics on unlicensed theme usage and lost revenue by evaluating temporal interval overlaps between installation lifespans and calendar months.

  Data Manipulation (SQL/Python)Technical ScreenSQLPandasInterval Overlap
• ShopifyMedium·Data Scientist
  Analyze Pirated Theme Usage Impact

  This question evaluates a candidate's ability to perform data manipulation and analytics with SQL and Python to detect license compliance, construct month-level time windows, and quantify revenue impact from pirated theme usage.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Analyze Trends to Optimize Pirate-Theme Product Strategy

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Analyze Trends to Optimize Pirate-Theme Product Strategy states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationTechnical Screen
• ShopifyMedium·Data Scientist
  Assess and push back on ideology-heavy interviews

  This question evaluates a candidate's competency in cultural fit assessment, evidence-backed critique, concise written and verbal communication, prioritization under time pressure, and the ability to design escalation and interview strategies.

  Behavioral & LeadershipTechnical Screen
• ShopifyMedium·Data Scientist
  Calculate Week-over-Week Revenue Change Using SQL

  This question evaluates proficiency in SQL time-series aggregation, weekly grouping, and prior-period comparison for calculating percentage changes in revenue.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Compute Jaccard Similarity for Lists

  This question evaluates understanding of Jaccard similarity and set-based operations, assessing competency in deduplicating lists, precise string comparisons (case-sensitivity and whitespace), and handling edge cases.

  Coding & AlgorithmsTechnical Screen
• ShopifyMedium·Data Scientist
  Compute Theme Similarity

  This question tests the ability to implement a set-based similarity metric, specifically the Jaccard index, to compare collections of theme-related features.

  Coding & AlgorithmsTechnical ScreenData StructuresAlgorithmsSets
• ShopifyEasy·Data Scientist
  Deep dive a technical project and its impact

  This question evaluates a data scientist's ability to lead and execute end-to-end technical projects, including problem framing, data sourcing and quality, modeling and experimentation, deployment and monitoring, and quantifying business impact.

  Behavioral & LeadershipTechnical Screen
• ShopifyMedium·Data Scientist
  Describe Your Professional Growth and Key Learning Experiences

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Professional Growth and Key Learning Experiences states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• ShopifyMedium·Data Scientist
  Describe toughest project and align stakeholders remotely

  This question evaluates leadership, end-to-end project ownership, cross-functional stakeholder alignment, decision-making under constraints, and the ability to quantify business impact in data science initiatives.

  Behavioral & LeadershipTechnical Screen
• ShopifyMedium·Data Scientist
  Identify Pirate Themes Using Similarity Score Algorithm

  This question evaluates understanding of similarity scoring, set-based comparison of structured records, and practical data deduplication techniques commonly used in data science.

  Coding & AlgorithmsTechnical Screen
• ShopifyMedium·Data Scientist
  Implement Cosine Similarity Function for String Vectors

  This question evaluates understanding of vector-space similarity and text representation by asking for a cosine similarity computation over string-derived vectors, testing coding and algorithmic skills relevant to data science and text processing.

  Coding & AlgorithmsTechnical Screen
• ShopifyMedium·Data Scientist
  Implement an LRU Cache

  This question tests the ability to combine a hash map and a doubly linked list to achieve constant-time performance for cache operations.

  Coding & AlgorithmsTechnical ScreenHash MapDoubly Linked ListCache
• ShopifyEasy·Data Scientist
  Infer and justify non-trivial sequence patterns

  This question evaluates mathematical reasoning, sequence inference, and pattern-recognition competency by asking the solver to identify governing rules and justify missing terms across numeric sequences and a small grid.

  Coding & AlgorithmsTechnical Screen
• ShopifyEasy·Data Scientist
  Measure App Store success and debug funnel anomaly

  This question evaluates proficiency in product analytics, instrumentation design, experimentation planning, and funnel-level diagnostic analysis for an app marketplace, and falls squarely in the Analytics & Experimentation domain for data scientist roles.

  Analytics & ExperimentationTechnical Screen
• ShopifyMedium·Data Scientist
  Measure Pirate-Theme Usage and Revenue Growth Over Time

  This question evaluates the ability to design time-based usage metrics and compute monthly revenue through data manipulation using SQL or Python, focusing on aggregating event and transaction records by calendar month.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Perform no-calculator math accurately and fast

  This question evaluates mental arithmetic, rapid estimation, numerical bounds, and familiarity with exponentials and square-root approximations within the Statistics & Math category for a Data Scientist role.

  Statistics & MathTechnical Screen
• ShopifyHard·Data Scientist
  Present Piracy Trends to a PM

  This question evaluates a candidate's ability to interpret time-series analytics and revenue-impact estimates, reason about measurement biases and censoring, and communicate ambiguous statistical findings to product stakeholders.

  Analytics & ExperimentationTechnical Screen