Data Scientist Interview Questions

Showing 1591–1620 of 1971 results

• 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
  Determine Growth of Pirated Theme Installations Over Years

  This question evaluates proficiency in SQL-based data manipulation and analytics, focusing on computing time-based cohort percentages and conducting year-over-year trend analysis of merchant behavior.

  Data Manipulation (SQL/Python)Onsite
• ShopifyMedium·Data Scientist
  Diagnose Weekly Session Conversion Anomalies

  This question evaluates proficiency in time-series anomaly detection, conversion-rate analysis, data quality handling (especially parsing and validating week labels), segmentation by shop type and session-duration buckets, and causal reasoning about traffic composition effects.

  Analytics & ExperimentationOnsite
• 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
  Measure Shopify App Store Success

  This question evaluates a data scientist's competency in product-metrics design for a multi-sided app marketplace, including defining a north-star metric, per-side success and guardrail metrics, required dataset schemas and join keys, and criteria for evaluating ranking/search/recommendation experiments.

  Analytics & ExperimentationOnsite
• 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
• SnapchatMedium·Data Scientist
  Analyze OLS After Duplicating Every Observation

  This question tests your understanding of OLS coefficient estimation and standard error calculations under the scenario of duplicate data observations.

  Statistics & MathTechnical ScreenOLSLinear RegressionStatistics
• SnapchatMedium·Data Scientist
  Calculate Average Camera Switches per Session

  Tests the ability to write a PostgreSQL query using window functions (LAG) to compute the average number of specific state transitions per session while accounting for zero-transition sessions and avoiding integer division.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions
• SnapchatMedium·Data Scientist
  Compute CTR and metrics with pandas

  This question evaluates proficiency in time-series and event-level data manipulation with pandas, including CTR computation, event denoising and deduplication, sessionization, cohort analysis, temporal joins, and performance-aware vectorized operations within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• SnapchatMedium·Data Scientist
  Compute User Retention and Analyze Event Data

  This question evaluates a candidate's ability to perform SQL and Python data manipulation for event analytics, including cohort retention, aggregation of distinct purchasing users, median-of-daily-events calculations, and hourly time-series grouping.

  Data Manipulation (SQL/Python)Onsite
• SnapchatMedium·Data Scientist
  Compute same-day acceptance metrics last week

  This question evaluates proficiency in time-based data manipulation, SQL JOINs and aggregations, handling NULLs and missing reference rows, and computing acceptance and percentage metrics with careful UTC date boundary treatment.

  Data Manipulation (SQL/Python)Technical Screen
• SnapchatMedium·Data Scientist
  Count Users Whose First Daily Event Is App Launch

  Write a PostgreSQL query to identify each user's first event per calendar day and aggregate the distinct user count by operating system and device when that first event is an app launch. Tests window functions, tie-breaking logic, and multi-dimensional grouping.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions
• SnapchatMedium·Data Scientist
  Design A/B Tests for Banner Ad and Group-Story Feature

  Evaluates A/B test design for a banner ad and a group-story feature in a social app. Strong answers define monetization, engagement, safety, and retention metrics, handle accidental clicks for ads, account for network effects in group stories, and set launch criteria with guardrails.

  Analytics & ExperimentationOnsite
• SnapchatHard·Data Scientist
  Design an experiment for spam filtering impact

  This question evaluates experimental design and causal inference skills for measuring the effect of a stricter spam filter on same-day friend-request acceptance, covering hypothesis specification, interference and randomization choices, metric definition and windows, power calculation, and handling incomplete labels.

  Analytics & ExperimentationTechnical Screen
• SnapchatMedium·Data Scientist
  Diagnose Violations of Linear Regression Assumptions

  Tests a candidate's systematic understanding of ordinary least squares (OLS) regression assumptions, distinguishing how specific violations compromise unbiased point estimation, standard error inference, and predictive accuracy.

  Statistics & MathTechnical ScreenStatisticsLinear RegressionEconometrics
• SnapchatMedium·Data Scientist
  How to Update Bayesian Model for Concept Drift?

  How to Update Bayesian Model for Concept Drift? 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 & MathOnsite
• SnapchatMedium·Data Scientist
  Influence Cross-Functional Teams Without Formal Authority

  Evaluates behavioral and leadership storytelling for influencing cross-functional teams without formal authority. Strong answers use specific data science examples, quantify impact, show coachability from feedback, and explain prioritization when leadership, data, and UX point in different directions.

  Behavioral & LeadershipOnsite
• SnowflakeMedium·Data Scientist
  Build a cohort dashboard with Streamlit and SQL

  This question evaluates proficiency in cohort analysis using SQL window functions, time-based aggregation, ARPU calculation, and integration of data querying with a Streamlit visualization front end, including handling timezones and data-quality checks.

  Data Manipulation (SQL/Python)Onsite
• SnowflakeMedium·Data Scientist
  Contrast FCF vs NI; choose one statement

  This question evaluates financial statement analysis skills—specifically reconciling net income to free cash flow, identifying accounting and working-capital red flags, and selecting which single statement best informs credit risk, profitability trends, and cash runway, thereby testing valuation, solvency, and financial-modeling competency in the Statistics & Math domain. It is commonly asked because interviewers probe both conceptual understanding and practical application of accrual versus cash accounting, the effects of non‑cash charges, capex and working‑capital movements on valuation, and awareness of which metrics (for example FCF margin, interest coverage, and current ratio) become unreliable or unobtainable when only one statement is available.

  Statistics & MathTechnical Screen
• SnowflakeMedium·Data Scientist
  Describe navigating ambiguous, repetitive questioning

  This question evaluates a Data Scientist's skills in stakeholder communication, ambiguity management, diagnostic reasoning to identify misaligned goals or definitions, adaptive framing between technical and plain-language explanations, trade-off analysis under time pressure, and delivering measurable outcomes.

  Behavioral & LeadershipTechnical Screen
• SnowflakeHard·Data Scientist
  Design an A/B test for ML model launch

  This question evaluates a data scientist's competence in experimental design, statistical power analysis, sequential monitoring, covariate adjustment (e.g., CUPED), and practical operational concerns like guardrails, interference, and ramping for ML model launches.

  Analytics & ExperimentationTechnical Screen
• SnowflakeMedium·Data Scientist
  Design an analytic warehouse for event data

  This question evaluates a candidate's competency in data warehousing and engineering for analytics, including event-level schema and partitioning, slowly changing dimensions, idempotent ingestion and deduplication, derived metrics modeling, and query performance and cost trade-offs using SQL/Python.

  Data Manipulation (SQL/Python)Onsite
• SnowflakeHard·Data Scientist
  Design and validate a cost-sensitive classifier

  This question evaluates competence in cost-sensitive binary classification, handling delayed and imbalanced labels, calibration and decision-threshold selection, distributional-drift detection, monitoring, and low-latency deployment within the Machine Learning / Data Science domain.

  Machine LearningTechnical Screen
• SnowflakeHard·Data Scientist
  Implement DFS with cycle detection and topo order

  This question evaluates mastery of graph algorithms—specifically depth-first search (both recursive and iterative), directed cycle detection, topological ordering, and discovery/finish timestamping—and falls under the Coding & Algorithms domain.

  Coding & AlgorithmsTechnical Screen