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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 4201–4230 of 4616 results
- Two SigmaHardData ScientistExpected Number of Good Pairs in a Randomly Weighted Complete Graph
This question evaluates probabilistic reasoning, combinatorics, and order-statistics intuition by asking for the expected count of mutual maximum-edge neighbors (good pairs) in an edge-weighted complete graph with i.i.d. continuous weights.
Coding & AlgorithmsTechnical Screen - Two SigmaHardData ScientistExplain why the t-statistic helps
This question evaluates understanding of the t-statistic, standardized effect sizes, standard errors, hypothesis testing, and interpretation of statistical evidence within the Statistics & Math domain, and it targets conceptual understanding with practical application implications.
Statistics & MathTechnical Screen - Two SigmaHardData ScientistForecast bikes available at a station
This question tests the ability to design a robust time-series forecasting pipeline, specifically addressing feature engineering for cyclic temporal patterns and the selection of appropriate validation strategies for sequential data.
Machine LearningTechnical ScreenTime-SeriesFeature EngineeringForecasting - Two SigmaMediumData ScientistHow detect duplicate card records?
This question evaluates a candidate's competency in duplicate detection, record linkage, entity resolution, and data quality assessment within credit card transaction datasets.
Machine LearningTechnical Screen - Two SigmaEasyData ScientistHow to forecast bike dock demand
This problem tests your ability to design a predictive pipeline for spatio-temporal demand, focusing on feature extraction from time-series data and robust validation strategies.
Machine LearningTechnical ScreenTime-SeriesFeature EngineeringValidation - Two SigmaHardData ScientistHow would you forecast bike demand?
This problem tests your ability to design a robust machine learning pipeline for time-series demand forecasting, focusing on feature engineering for exogenous variables and evaluation metrics for spatial-temporal data.
Machine LearningTechnical ScreenTime-SeriesFeature EngineeringRegression - Two SigmaMediumSoftware EngineerImplement Price-Time Order Matching
Design and implement an in-memory limit order book matching engine supporting buy and sell orders with price-time priority (FIFO) and order cancellation.
Coding & AlgorithmsTechnical Screenheapdesignhash-table - Two SigmaMediumSoftware EngineerImplement a Hash Map with Separate Chaining and Resizing
This question evaluates understanding of hash table fundamentals, including hashing, separate chaining collision resolution, linked-list bucket management, dynamic resizing, and rehashing to maintain load factor and amortized performance.
Coding & AlgorithmsTechnical Screen - Two SigmaHardSoftware EngineerImplement an In-Memory Database
This question evaluates competence in implementing in-memory relational data structures, parsing tokenized database-like commands, and evaluating compound predicates with comparison operators.
Coding & AlgorithmsTechnical Screen - Two SigmaHardSoftware EngineerImplement binary encode/decode with frequency tree
This question evaluates understanding of frequency-based binary encoding and decoding, including deterministic tree construction, tie-breaking rules, and traversal, and tests knowledge of trees, priority queues, greedy algorithms, and bit-level string manipulation in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - Two SigmaMediumData ScientistImplement merge sort and largest 1-rectangle
This question evaluates proficiency in implementing and analyzing classic algorithms, specifically merge sort for array sorting and techniques for computing the largest all-ones rectangular submatrix in a binary matrix, testing algorithmic thinking, data structure manipulation, and time/space complexity reasoning.
Coding & AlgorithmsTechnical Screen - Two SigmaMediumQuantitative ResearcherLinear Interpolator with Extrapolation
This question evaluates understanding of piecewise linear interpolation and extrapolation, numerical robustness with floating-point values, and algorithmic competencies such as sorting and efficient query location for large datasets.
Coding & AlgorithmsTechnical Screen - Two SigmaMediumSoftware EngineerMatch Limit Orders with Price-Time Priority
Implement a limit order matching engine adhering to price-time priority (FIFO), resting-order execution pricing, and partial fill mechanics.
Coding & AlgorithmsTechnical ScreenHeapData StructuresSimulation - Two SigmaEasyData ScientistMatch readings with latest same-city humidity
This question evaluates the ability to align and join time-ordered sensor data by key, testing skills in time-series merging, per-key state management, and handling absent matches.
Coding & AlgorithmsTechnical Screen - Two SigmaMediumSoftware EngineerMaximize Capital by Completing at Most k Projects
This question evaluates algorithmic problem-solving and optimization skills within the Coding & Algorithms domain, focusing on selection under resource and operation constraints and reasoning about time and space complexity.
Coding & AlgorithmsTechnical Screen - Two SigmaHardData ScientistMerge Two Sorted Lists
This problem tests your ability to manipulate linked list pointers to merge two sorted sequences efficiently without allocating extra space for new nodes.
Coding & AlgorithmsTechnical ScreenLinked ListsPointersRecursion - Two SigmaHardData ScientistMerge two sorted linked lists
This problem tests your ability to manipulate linked list pointers to merge two sorted structures in-place, focusing on space optimization and handling edge cases.
Coding & AlgorithmsTechnical ScreenLinked ListsPointersIn-place - Two SigmaHardData ScientistPredict Citi Bike Demand at a Specific NYC Station
This question evaluates time-series forecasting, demand estimation, and ML system-design competencies, focusing on problem framing, handling censored observations, data integration, model selection, and evaluation under operational constraints.
ML System DesignTechnical Screen - Two SigmaMediumData ScientistPredict Stock Prices from Google Search Data
This question evaluates a candidate's competency in time-series and cross-sectional predictive modeling, alternative-data feature engineering, and rigorous validation to prevent temporal leakage within the Machine Learning domain.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredict a Company's Stock Price from Six Years of Web Search Logs
This question evaluates proficiency in feature engineering and time-series forecasting from large-scale event logs, including entity linking, aggregation and normalization, handling non-stationarity and drift, and model selection and validation for predicting a given company's stock price.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredict bike demand and avoid overfitting
This question evaluates time-series forecasting, feature engineering, awareness of data leakage risks, model evaluation choices, and overfitting prevention competencies within the Machine Learning domain applied to demand prediction.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredicting Stock Prices from Twitter Data
This question evaluates competency in ML system design for integrating NLP-derived signals from social media with time-series financial modeling, including feature extraction, timestamp alignment, and handling low signal-to-noise ratios.
ML System DesignTechnical Screen - Two SigmaMediumData ScientistReturn sorted values after quadratic transform
This problem tests your ability to efficiently transform elements in a sorted array using a quadratic function and merge them back into a single sorted array in linear time.
Coding & AlgorithmsTechnical ScreenArraysTwo PointersSorting - Two SigmaHardData ScientistSlopes of y-on-x vs x-on-y Regression: Relationship and When They Are Equal
This question evaluates understanding of simple OLS linear regression estimators, moment-based expressions for slopes, the relationship between y-on-x and x-on-y fitted slopes, and how correlation and variable scaling influence coefficient values.
Machine LearningTechnical Screen - Two SigmaMediumData ScientistWhy Is the Sample Mean Approximately Normal for Large Samples?
This question evaluates understanding of the Central Limit Theorem and asymptotic statistical reasoning, including recognition that the standardized sample mean (equivalently the standardized sum) becomes approximately normal and how that fact underpins inference such as confidence intervals.
Machine LearningTechnical Screen - Two SigmaHardData ScientistWhy Use a t-Statistic?
This problem tests your conceptual understanding of the t-statistic, specifically how sample parameters influence its value and how it bridges the gap between estimation and hypothesis testing.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingQuantitative Finance - Two SigmaEasyData ScientistWhy is the t-statistic useful
This question tests the candidate's conceptual understanding of the t-statistic, specifically its role in hypothesis testing when population parameters are unknown.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingInference - Two SigmaHardData ScientistWhy use the t-statistic?
This question tests the conceptual understanding of the t-statistic, specifically why it is used instead of the Z-statistic when population parameters are unknown and how it accounts for uncertainty in sample variance.
Statistics & MathTechnical ScreenStatisticsHypothesis TestingT-Distribution - UberMediumData ScientistAnalyze Cancellation Change with Statistics
This question evaluates statistical inference for binary outcomes, regression adjustment for confounding, experiment design and power analysis, and nonparametric uncertainty estimation using bootstrapping, and is targeted at Data Scientist roles within the Statistics & Math domain.
Statistics & MathTechnical Screen - UberEasyMachine Learning EngineerAnalyze sales with groupby
This question evaluates proficiency in data manipulation and analytics using pandas, specifically the ability to join relational tables and apply groupby aggregations to compute business metrics like total sales, average sales per order, and completion rates.
Coding & AlgorithmsTechnical Screen
Two Sigma
Uber