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Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
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- 546
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- 11
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- 8
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Showing 481–510 of 641 results
- PayPalMediumData ScientistDesign elevator scheduling for small building
Design elevator scheduling for small building evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - PayPalEasyData ScientistDesign metrics and experiment for donation feature
A PayPal data science onsite case asking you to design metrics and a randomized experiment for an optional checkout donation feature on a food-ordering platform. Covers goal and hypothesis framing, a primary/diagnostic/guardrail metric hierarchy with precise definitions, user-level experiment design with power and variance-reduction, confounder/marketplace-bias handling, and a launch decision framework.
Analytics & ExperimentationOnsite - PayPalMediumData ScientistExplain Window Functions and Joins in SQL and Python
This question evaluates proficiency in SQL and Python data manipulation, covering window functions, join semantics (INNER, LEFT, RIGHT, FULL, CROSS), ranking functions, group and string aggregation, type casting, and building row-wise accumulations in Python, and it falls under the Data Manipulation (SQL/Python) category.
Data Manipulation (SQL/Python)Onsite - PayPalEasyData ScientistExplain confounding with an Uber example
This Statistics & Math interview question for a Data Scientist tests understanding of confounding and causal inference in observational data. Candidates must define a confounder, give a concrete non-demographic Uber example identifying the exposure, outcome, and confounder with the direction of bias, and describe at least two mitigation methods along with the assumptions each requires.
Statistics & MathOnsite - PayPalEasyData ScientistExplain p-values and interpret regressions
A PayPal Data Scientist onsite statistics rapid-fire covering p-values (plain-language and formal definitions, common misinterpretations, and an A/B-test decision at p=0.03), interpreting linear-regression coefficients under confounding (associational vs causal, 'controlling for' failure modes, and bias-reduction methods), and L1 vs L2 regularization with how to choose the penalty strength. It tests both statistical depth and the ability to communicate to product and technical audiences.
Statistics & MathOnsite - PayPalMediumData ScientistExplain past experience and role fit
Explain past experience and role fit evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - PayPalHardData ScientistExplain unsupervised fraud and evaluation
Explain unsupervised fraud and evaluation evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - PayPalMediumData ScientistHow to validate production models?
This question assesses the end-to-end strategy for validating a production-ready machine learning model, focusing on rigorous testing frameworks, drift detection, and business-risk mitigation.
Machine LearningOnsiteModel ValidationProduction MLFraud Detection - PayPalMediumData ScientistIdentify Session with Maximum Overlapping Sessions Count
This question evaluates temporal interval reasoning, set-based querying and aggregation skills, focusing on identifying and counting overlapping time intervals using SQL or equivalent Python data-manipulation tools.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistIdentify Users with Specific Page Visit Sequence
This question evaluates event-sequence detection and temporal ordering skills in SQL, focusing on concepts such as windowing, adjacent-row reasoning, and session-scoped grouping to identify ordered page visits.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistInfluence Stakeholders Without Authority: Strategies and Examples
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Influence Stakeholders Without Authority: Strategies and Examples states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - PayPalMediumData ScientistMaster A/B Testing: Key Concepts and Methodologies Explained
Evaluates practical A/B testing and causal inference fundamentals for online products. Strong answers cover p-values, errors, power, workflow, Simpson's paradox, metrics, and observational causal methods.
Analytics & ExperimentationOnsite - PayPalEasyData ScientistReduce airport ride cancellations under causal constraints
A PayPal Data Scientist onsite product-sense and causal-inference case: design a measurement framework, hypotheses, and experimentation strategy to reduce airport ride cancellations in a ride-hailing marketplace without harming marketplace health. It tests metric trees, interference-aware experiment design (switchback, partial-population, encouragement/IV), satisfaction proxies, confounder handling, and stakeholder communication.
Analytics & ExperimentationOnsite - PayPalMediumData ScientistResolve Conflicts in Data Science Leadership Scenarios
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Resolve Conflicts in Data Science Leadership Scenarios states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - PayPalEasyData ScientistShould you play a dice payout game?
This question evaluates understanding of probability, expected value, and payoff-driven decision-making by requiring computation of expected return from discrete game outcomes. It is commonly asked to assess quantitative reasoning in the Statistics & Math category and primarily tests conceptual understanding of expected value and probabilistic reasoning rather than practical implementation.
Statistics & MathOnsite - PayPalMediumData ScientistWrite conditional aggregates with CASE WHEN
Write conditional aggregates with CASE WHEN evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistWrite conditional aggregation SQL queries
This question evaluates proficiency with conditional aggregation in SQL, including the use of SUM(CASE WHEN ...) versus filtering with WHERE and the implications of placing aggregation filters in HAVING for grouped queries, within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Onsite - PinterestMediumData ScientistAnalyze Global Engagement and Impressions with SQL Queries
This question evaluates proficiency in SQL-based data manipulation and analytical querying, covering aggregation, grouping, ranking, and the correlation of impressions with user engagement and feature usage metrics.
Data Manipulation (SQL/Python)Onsite - PinterestHardData ScientistAnalyze a geo rollout and interpret charts
This question evaluates causal inference and product analytics competencies — specifically specification of causal estimands and difference-in-differences, segmented (interrupted) time-series regression, power and duration calculations, spillover and donor-pool diagnostics, and selection of guardrail metrics.
Analytics & ExperimentationOnsite - PinterestHardData ScientistAnalyze survey with gender imbalance
Analyze survey with gender imbalance 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 - PinterestMediumData ScientistCalculate 95% Bootstrap Confidence Interval for Order Values
This question evaluates a data scientist's competence in statistical inference using bootstrap resampling, proficiency with numerical computing for large sample operations, and attention to performance optimization.
Coding & AlgorithmsOnsite - PinterestMediumData ScientistClean and Aggregate Transactions for Finance Dashboard
This question evaluates data cleaning and transformation competencies including pandas-based vectorized operations, categorical mapping and feature flagging, as well as aggregation and summarization of transactional data in the data manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Onsite - PinterestHardData ScientistConnect Probability, Experiment Sizing, and Bootstrap Inference
Tests end-to-end statistical rigor across discrete probability modeling, sample size derivation for A/B testing, bootstrap confidence intervals for heavy-tailed engagement metrics, and translating conflicting inferential signals into product launch recommendations.
Statistics & MathOnsiteStatisticsExperimentationA/B Testing - PinterestHardData ScientistDecide if ad load is optimized
This question evaluates competency in analytics and experimentation, covering experiment design, metric and guardrail definition, causal inference, statistical testing, segmentation for heterogeneous effects, and balancing user experience versus monetization.
Analytics & ExperimentationOnsite - PinterestMediumData ScientistDecode and Explain Ambiguity in Compression Strings
This question evaluates parsing of compact count-value encodings, reasoning about ambiguous encodings, and combinatorial enumeration of all valid decodings.
Coding & AlgorithmsOnsite - PinterestHardData ScientistDemonstrate leadership with concrete STAR examples
This question evaluates leadership, stakeholder influence, metric-driven decision-making, ambiguity management, end-to-end analytical ownership, and resilience in a Data Scientist context, categorized under Behavioral & Leadership.
Behavioral & LeadershipOnsite - PinterestMediumData ScientistDesign Algorithm to Minimize Payments in Expense-Sharing App
This question evaluates algorithm design and optimization skills, focusing on modeling multi-party transactions and minimizing the number of settlement operations.
Coding & AlgorithmsOnsite - PinterestMediumData ScientistDesign Data Structure for Sparse Matrices Operations
This question evaluates a candidate's ability to design efficient data structures and implement algorithms for sparse matrix storage and operations, emphasizing space-efficient representations, matrix arithmetic, and computational complexity reasoning.
Coding & AlgorithmsOnsite - PinterestHardData ScientistDesign rigorous A/B test and causal analysis
This question evaluates a data scientist's competency in experimental design, sample-size and power calculations, variance-reduction methods (e.g., CUPED), sequential testing and alpha spending, clustering and interference effects, SRM checks, and causal identification strategies such as DID, IV, and RDD within the Statistics & Math domain.
Statistics & MathOnsite - PinterestMediumData ScientistDetermine Pin Connections Through Common Boards
This question evaluates graph modeling and connectivity concepts, including representing boards and pins as relationships and reasoning about reachability and minimal connection paths.
Coding & AlgorithmsOnsite
PayPal
Pinterest