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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.
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- 546
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Showing 1381–1410 of 1971 results
- PayPalMediumData ScientistClean and Summarize User Purchase Data Efficiently
This question evaluates proficiency in data cleaning, aggregation, and time-series manipulation within the Data Manipulation (SQL/Python) domain, focusing on filtering by group counts and calculating inter-event timing metrics.
Data Manipulation (SQL/Python)Onsite - PayPalMediumData ScientistCompare WHERE vs HAVING with aggregates
Compare WHERE vs HAVING with aggregates 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 - PayPalHardData ScientistCompute Variance from a Python List
This question evaluates understanding of basic statistical concepts and numerical computation—specifically population versus sample variance—and tests algorithmic implementation skills, edge-case handling, and complexity reasoning within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - PayPalEasyData ScientistCompute variance of a list in Python
This question evaluates understanding of statistical measures and numeric computation, focusing on implementing variance calculations (population vs sample) and considerations for numerical stability.
Coding & AlgorithmsTechnical Screen - PayPalMediumData ScientistCount Word Frequency and Print Top Three Words
This question evaluates proficiency in basic Python programming, string/text processing, frequency counting and sorting algorithms, reflecting core competencies for data manipulation tasks.
Coding & AlgorithmsOnsite - PayPalEasyData ScientistDecide if a dice-betting game is favorable
This question evaluates understanding of probability theory, expected value computation, risk-neutral decision criteria, and basic Monte Carlo simulation concepts within the Statistics & Math domain, testing both conceptual understanding of discrete probabilistic outcomes and practical application of simulation-based verification.
Statistics & MathOnsite - PayPalEasyData ScientistDescribe career goals and what makes good teams
This Behavioral & Leadership interview prompt for a Senior Data Scientist evaluates leadership, cross-functional influence, strategic communication, career planning, and team-operating competencies by requesting a data-driven project example, a 3–5 year career outlook, and definitions of effective managers and teams.
Behavioral & LeadershipOnsite - PayPalHardData ScientistDesign an A/B for ATO rule
This question evaluates a data scientist's experiment-design and statistical-analysis competencies, including cluster-aware randomization, power/sample-size calculation, sequential monitoring, and translating detectable effects into net business impact for a real-time account-takeover prevention rule on a payment platform.
Analytics & ExperimentationTechnical Screen - PayPalMediumData ScientistDesign and Analyze A/B Test for Cashback Program
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 Design and Analyze A/B Test for Cashback Program states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - PayPalEasyData ScientistDesign and evaluate a fraud detection strategy
This problem tests your ability to design a holistic fraud detection strategy by balancing technical model metrics with business-critical operational trade-offs.
Analytics & ExperimentationTechnical ScreenFraud DetectionClassificationMetrics - 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 - PayPalHardData ScientistExplain differences between Python list and tuple
Evaluates understanding of Python data structures—specifically lists versus tuples—by probing concepts such as mutability and its implications, high-level performance and memory trade-offs, typical use cases, and hashability for use as dictionary keys.
Coding & AlgorithmsTechnical Screen - 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 - PayPalEasyData ScientistHow to evaluate a new homepage feature
A PayPal data science technical screen on designing an A/B test to evaluate a new homepage feature. It covers experiment design and randomization, primary/secondary/guardrail metric selection with tradeoffs, sample-size and power estimation, and pitfalls like novelty effects and interference. The solution emphasizes value-aligned metrics (payment completion over CTR) and fraud guardrails specific to a payments product.
Analytics & ExperimentationTechnical Screen - 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 ScientistImplement sliding-window device anomaly
This question evaluates understanding of streaming algorithms, sliding-window state management, per-user data structures, and handling out-of-order events under strict time and space complexity constraints.
Coding & AlgorithmsTechnical Screen - 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 - PayPalHardData ScientistInterpret p-values and common pitfalls
Evaluates interpretation of p-values, hypothesis testing, experimental design and statistical pitfalls in a fraud/experimentation context—including concepts like effect size, statistical power, multiple comparisons, delayed/rare outcomes, and imperfect randomization; category/domain: Statistics & Math.
Statistics & MathTechnical Screen - 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 SQL to flag Venmo ATO
This question evaluates a candidate's ability to construct complex SQL to detect account-takeover (ATO) signals in a payments platform, assessing skills in event-time windowing, deduplication, joins across user/login/transaction tables, and categorical flagging of new devices or IPs.
Data Manipulation (SQL/Python)Technical Screen
PayPal