Data Scientist Interview Questions

Showing 151–180 of 641 results

• Capital OneEasy·Data Scientist
  Visualize Price Impact on Demand and Profit Trends

  Evaluates price sensitivity and profit-curve reasoning for a network service with downward-sloping demand. Strong answers explain demand curves, profit shape, marginal revenue, marginal cost, and optimal price.

  Analytics & ExperimentationOnsite
• Capital OneMedium·Data Scientist
  Write SQL for theme-park revenue and visits

  This question evaluates SQL data-manipulation and analytical skills, including joins, aggregations, date-range filtering, handling multiple ticket ownership, and edge cases like zero counts when computing revenue and visit metrics.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Write SQL to compute campaign net revenue

  This question evaluates SQL-based data manipulation and analytics skills, including aggregation, deduplication, date-range filtering, revenue and cost calculations, and selection logic for prioritizing additional donors.

  Data Manipulation (SQL/Python)Onsite
• CitadelHard·Data Scientist
  Solve Classic Probability Questions

  This problem tests your ability to calculate the expected stopping time of a stochastic process and your proficiency with the linearity of expectation in the context of sequential probability.

  Statistics & MathOnsiteProbabilityExpectationStochastic Processes
• CitadelHard·Data Scientist
  Solve probability and expectation problems

  This set of problems evaluates probabilistic reasoning and expectation skills across discrete and continuous settings, covering conditional probability with evolving information, convolution and distribution derivation, occupancy/collector problems, symmetry-based variance on manifolds, and sequential selection/optimal stopping.

  Statistics & MathOnsite
• CitadelHard·Data Scientist
  Sort a nearly sorted array

  This question evaluates algorithmic problem-solving skills, specifically understanding sorting under constrained inputs and rigorous time and space complexity analysis.

  Coding & AlgorithmsOnsite
• CoinbaseMedium·Data Scientist
  Calculate Conversion Lift CI

  This question tests the ability to derive and calculate a confidence interval for the difference in conversion rates between two groups, and to explain how this interval informs a business decision.

  Statistics & MathOnsiteStatisticsA/B TestingInference
• CoinbaseMedium·Data Scientist
  Calculate a Confidence Interval

  This question evaluates a candidate's competency in statistical inference for A/B testing, specifically the construction and interpretation of confidence intervals for differences in proportions, and falls under the Statistics & Math domain relevant to Data Scientist roles; it tests practical application (deriving point estimates and standard errors) alongside conceptual understanding (assumptions behind normal approximation versus exact, score-based, or bootstrap intervals). Interviewers commonly ask this to assess ability to quantify treatment effects, reason about approximation validity and alternative interval methods, and distinguish between absolute and relative lift when reporting experimental results.

  Statistics & MathOnsite
• CoinbaseMedium·Data Scientist
  Compute adoption, latency, and cross-region transactions

  This question evaluates proficiency in SQL-based data manipulation and analytics, including calculating cohort adoption and transaction rates, performing date arithmetic for time-to-first-transaction, identifying cross-region transactions, and computing distributional percentiles.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Data Scientist
  Design Identity & Trust Experiment

  This problem tests your ability to design a robust A/B test for a high-stakes identity verification flow, focusing on balancing security metrics with user acquisition and handling potential network interference.

  Analytics & ExperimentationOnsiteA/B TestingProduct MetricsPower Analysis
• CoinbaseMedium·Data Scientist
  Design an Identity Trust Experiment

  This question tests your ability to design a randomized controlled trial to measure the impact of a new identity verification process on user trust and retention, while addressing potential network effects and platform bias.

  Analytics & ExperimentationOnsiteExperimentationCausal InferenceA/B Testing
• CoinbaseMedium·Data Scientist
  Implement Plus One

  This question evaluates array manipulation and numeric carry propagation skills, along with the ability to reason about time and space complexity when performing arithmetic on digit lists.

  Coding & AlgorithmsOnsite
• CoinbaseMedium·Data Scientist
  Increment Digit Array

  This question tests the ability to handle array-based arithmetic and carry propagation logic, specifically addressing edge cases like array resizing.

  Coding & AlgorithmsOnsiteArraysMathSimulation
• CoinbaseMedium·Data Scientist
  Write SQL: sum values ≤ each row’s value

  This question evaluates proficiency in SQL aggregation, query optimization, and handling duplicate values when computing cumulative sums across rows. It is commonly asked to assess practical ability in the Data Manipulation (SQL/Python) domain to design scalable, efficient queries and demonstrates a focus on practical application of query-design and optimization rather than purely conceptual theory.

  Data Manipulation (SQL/Python)Onsite
• ConfluentMedium·Data Scientist
  Determine Tutorial Completion Rate by Variant in 7 Days

  This question evaluates a candidate's competency in data manipulation and event-based analytics, focusing on skills such as joining user and event tables, aggregating event data, and computing cohort completion metrics by variant.

  Data Manipulation (SQL/Python)Onsite
• ConfluentMedium·Data Scientist
  Evaluate Metrics and Randomization for Onboarding Tutorial Change

  Evaluate Metrics and Randomization for Onboarding Tutorial Change evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Analytics & ExperimentationOnsite
• DRWMedium·Data Scientist
  Compute Markov steady state and expectations

  Compute Markov steady state and expectations 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
• DRWEasy·Data Scientist
  Derive eigenvalues and sum for inverse matrix

  Derive eigenvalues and sum for inverse matrix 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
• DRWMedium·Data Scientist
  Solve Markov and distribution expectation problems

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

  Statistics & MathOnsite
• DRWMedium·Data Scientist
  Solve movie ratings, array, release scheduler

  This question tests algorithmic problem-solving across three domains: greedy array optimization, tree-based data structure design, and efficient sequence merging. It evaluates practical coding ability and complexity analysis, commonly used in quantitative and data science interviews to assess how candidates identify inefficiencies and select the right data structure to improve performance.

  Coding & AlgorithmsOnsite
• DRWMedium·Data Scientist
  Solve three algorithmic tasks in Python

  This question evaluates proficiency in dynamic programming, range-query data structures (Fenwick/segment tree), sorting and two-pointer techniques, and algorithmic complexity analysis within the Coding & Algorithms domain for Python-based implementation.

  Coding & AlgorithmsOnsite
• DoorDashMedium·Data Scientist
  Boost App Installs: Analyze and Experiment with Conversion Funnel

  DoorDash analytics prompt on improving mobile web to app-install conversion, covering funnel measurement, attribution, cohorts, install and activation experiments, incentives, guardrails, and long-term retention impact.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Build ETA prediction and simulate impact

  This question evaluates machine learning competencies for ETA prediction, including feature engineering, leakage detection, time-based validation, point and quantile modeling, evaluation and calibration metrics, cost-sensitive decisioning, explainability and fairness auditing, and production deployment considerations.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Build a late-delivery risk model

  This question evaluates competencies in production-ready predictive modeling, including target definition, feature engineering, temporal train/validation splitting to avoid leakage, model evaluation and calibration, monitoring and ramp strategies, and translating probabilistic outputs into business-impacting decisions for a Data Scientist.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Calculate Order Request Metrics

  This question evaluates SQL and data-manipulation competencies for computing order-level metrics, including aggregations, joins, windowed event sequencing, timestamp/timezone handling, and NULL-aware comparisons.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Compute rolling cold-delivery rates with windows

  This question evaluates proficiency with SQL window functions, time-based joins, event deduplication and business-rule implementation for computing rolling metrics over temporal windows.

  Data Manipulation (SQL/Python)Onsite
• DoorDashHard·Data Scientist
  Decide and test a 20% discount strategy

  This question tests the ability to design a controlled experiment to evaluate a promotional discount strategy, focusing on causality, profit optimization, and long-term customer value impact.

  Analytics & ExperimentationOnsiteCausal InferenceExperimentationUplift Modeling
• DoorDashHard·Data Scientist
  Decompose and optimize delivery operational costs

  This question evaluates a data scientist's ability to decompose per-order operational costs, identify high-impact levers to reduce unit economics, and design rigorous experiments and monitoring for a two-sided delivery marketplace, testing competencies in cost modeling, causal inference, experimentation design, and product analytics.

  Analytics & ExperimentationOnsite
• DoorDashHard·Data Scientist
  Define and compute retention and churn precisely

  This question evaluates a Data Scientist's competency in statistical measurement of user retention and churn, covering cohort definition, activity rules, risk-set-aware retention and churn formulas, censoring and delayed conversion handling, and survival-analysis concepts like time-to-churn, hazard, and cumulative incidence.

  Statistics & MathOnsite
• DoorDashHard·Data Scientist
  Define metrics for new market expansion success

  This question tests the ability to design a comprehensive evaluation framework for entering a new geographic market by balancing growth, unit economics, and marketplace balance.

  Analytics & ExperimentationOnsiteAnalyticsMarketplaceKPIs