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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
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 1771–1800 of 1971 results
- Two SigmaMediumData ScientistEvaluate Piecewise Linear Function
This question tests the ability to efficiently search through sorted interval data and apply linear interpolation to compute the value of a continuous piecewise function at a specific input point.
Coding & AlgorithmsOnline AssessmentBinary SearchInterpolationData Structures - Two SigmaMediumData ScientistEvaluate a Piecewise Linear Function
This question tests the ability to efficiently search through a sorted collection of coordinates and apply linear interpolation to compute values for a continuous piecewise function.
Coding & AlgorithmsOnline AssessmentBinary SearchInterpolationGeometry - Two SigmaMediumData ScientistEvaluate piecewise linear function at x
This question evaluates competency in numerical interpolation and handling of piecewise linear functions, including locating the correct segment in a sorted sequence of points and managing out-of-range inputs.
Coding & AlgorithmsOnline Assessment - 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 SigmaEasyData ScientistFill Missing Time-Series Values with Linear Interpolation (Duplicate Timestamps Allowed)
This question evaluates a candidate's ability to perform time-series data cleaning and aggregation of duplicate timestamps, plus numerical linear interpolation and boundary value propagation, within the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - 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 SigmaHardData ScientistImplement Univariate Linear Regression with Ordinary Least Squares
This question evaluates understanding of univariate linear regression and the ordinary least squares (OLS) closed-form estimator, probing competencies in basic statistical estimation, interpretation of slope and intercept, numerical stability, and floating-point arithmetic.
Coding & AlgorithmsOnsite - 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 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 SigmaHardData ScientistMaximum-Product Simple Path in a Complete Directed Graph
This question evaluates skills in graph algorithms, multiplicative path optimization, and combinatorial dynamic programming, testing competency in reasoning about simple paths and product-based scoring in directed weighted graphs.
Coding & AlgorithmsOnsite - 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 SigmaMediumData ScientistPerform no-intercept linear regression from two datasets
This question evaluates linear regression modeling, numerical linear algebra (normal equations and stable factorization methods), regularization, R² interpretation, and data-preprocessing/validation skills within the Machine Learning domain for a Data Scientist role.
Machine LearningOnline Assessment - 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 - UberHardData ScientistApply instrumental variables under interference
This question evaluates understanding of causal inference with instrumental variables in the presence of interference, testing skills in defining units and aggregation levels for market‑level spillovers, articulating IV assumptions (relevance, exclusion restriction, independence, monotonicity), and formulating estimation frameworks such as two‑stage least squares. Commonly asked in Statistics & Math interviews for data scientist roles because networked marketplaces invalidate simple A/B tests, it sits in the econometrics/causal inference domain and primarily assesses practical application of IV methods while requiring conceptual understanding of identification, robustness diagnostics, and sensitivity analysis.
Statistics & MathTechnical Screen
Two Sigma
Uber