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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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Showing 6751–6780 of 9403 results
- PayPalHardData ScientistBuild a real-time ATO model
This question assesses the ability to design an end-to-end, low-latency machine learning pipeline for detecting account takeover (ATO) in payment transactions, focusing on label noise, feature engineering, and system deployment constraints.
Machine LearningTechnical ScreenReal-time SystemsFraud DetectionFeature Engineering - PayPalEasyData ScientistCalculate Probability of Heads in Coin Flip Experiment
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Probability of Heads in Coin Flip Experiment states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - 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 - PayPalMediumSoftware EngineerCompare final, finally, finalize
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compare final, finally, finalize states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - PayPalMediumSoftware EngineerCompare write-back vs write-through caches
This question evaluates understanding of caching policies and memory/storage hierarchy trade-offs, focusing on how write-back and write-through handle writes, coherence, durability, latency, bandwidth, dirty bits, write amplification, and power-loss risks.
Coding & AlgorithmsTechnical Screen - 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 - PayPalMediumSoftware EngineerContrast TCP vs UDP; detect loss
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Contrast TCP vs UDP; detect loss states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
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 - PayPalMediumSoftware EngineerDesign a Cross-Border Money Transfer Service
This question evaluates proficiency in designing distributed, fault-tolerant payment systems with competencies in payments flows, exchange-rate management, compliance and regulatory constraints, transactional integrity, idempotency, scalability, reliability, and observability.
System DesignOnsite - PayPalMediumSoftware EngineerDesign a Payment Fraud Detection Service
This system design question tests the ability to architect a real-time, low-latency fraud decisioning service that combines deterministic rule engines with machine learning model scores. It evaluates practical understanding of distributed systems trade-offs, feature store design, and safe model/rule deployment on a high-availability critical path.
System DesignOnsite - PayPalHardMachine Learning EngineerDesign a traditional fraud detection system
This question evaluates a Machine Learning Engineer's competency in end-to-end ML system design for real-time payments fraud detection, including labeling under delayed confirmations, handling extreme class imbalance and sampling, feature engineering across behavioral, graph, device and merchant signals, model selection for latency and scale, and production scoring and monitoring architecture. It is commonly asked in the ML System Design category to assess how an engineer balances low-latency decision-making with delayed sparse labels, calibration and threshold trade-offs, operational scalability and resiliency, and drift/adversarial detection, testing both conceptual understanding and practical application.
ML System DesignOnsite - 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 - PayPalMediumSoftware EngineerDetect memory leaks in C++
This interview question evaluates engineering fundamentals, trade-offs, examples, pitfalls, and operational implications in a realistic interview setting. A strong answer for Detect memory leaks in C++ states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsTechnical Screen - PayPalMediumSoftware EngineerExplain HashMap internals and collisions
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Explain HashMap internals and collisions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - PayPalMediumSoftware EngineerExplain Java volatile semantics
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Explain Java volatile semantics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - 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 - PayPalMediumSoftware EngineerFind k most frequent in linear time
This question evaluates a candidate's ability to design and analyze efficient frequency-counting algorithms and related data structures, testing competencies in hashing, bucket-based grouping, heap usage, and time/space complexity reasoning.
Coding & AlgorithmsTechnical Screen
PayPal