Interview Questions

Showing 4231–4260 of 4616 results

• UberHard·Data Scientist
  Apply 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
• UberMedium·Software Engineer
  Bracket substrings matching any pattern

  This problem tests your ability to efficiently match and replace substrings based on a set of predefined patterns, prioritizing matches based on position and length.

  Coding & AlgorithmsTechnical ScreenStringsGreedyPattern Matching
• UberMedium·Software Engineer
  Build a Quadtree and Analyze a Graph

  This question evaluates skills in spatial data structures and recursive tree construction (building a quadtree) as well as graph analysis using traversal and cycle/safety detection to identify nodes that necessarily reach terminal nodes.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Data Scientist
  Build cold-start restaurant ratings

  This problem tests the ability to design an end-to-end machine learning system for predicting restaurant ratings when historical user-item interactions are scarce, focusing on feature engineering, model selection, and bias mitigation.

  Machine LearningTechnical ScreenCold-StartRegressionFeature Engineering
• UberMedium·Data Scientist
  Can one car serve all riders?

  This problem tests your ability to determine if a single resource (a car) can accommodate a series of scheduled tasks without overlap, requiring efficient sorting and comparison techniques.

  Coding & AlgorithmsTechnical ScreenSortingIntervalsGreedy
• UberMedium·Data Scientist
  Compare Two Coin Proportions

  This question evaluates proficiency in statistical inference for proportions, including hypothesis formulation, comparison of two independent samples, confidence interval interpretation, and interpretation of p-values and sampling variability.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Compute A/B sample size under clustering

  This question evaluates competency in experimental design and statistical inference for A/B testing, specifically sample size calculation for two-sample proportions with unequal allocation, clustering effects (ICC and design effect), and adjustments for bot removal and data-quality attrition.

  Statistics & MathTechnical Screen
• UberMedium·Data Scientist
  Compute CDF from a PDF Function

  This problem tests your understanding of numerical integration techniques (specifically the trapezoidal rule) and your ability to implement a functional mapping of a continuous probability density function (PDF) into a discrete cumulative distribution function (CDF).

  Coding & AlgorithmsTechnical ScreenNumerical MethodsProbabilityIntegration
• UberMedium·Data Scientist
  Compute ETA shift and conversion uplift

  This question evaluates proficiency in SQL and Python for time‑series data manipulation and experimental analysis, covering window functions (7‑day percentile_cont medians), date spines and timezone‑aware timestamp truncation, conversion-rate aggregation, difference‑in‑differences uplift estimation, and CUPED adjustment with cluster‑robust standard errors. It is commonly asked to assess practical data engineering and applied statistics skills in the Data Manipulation (SQL/Python) domain, testing applied implementation and causal inference ability rather than only conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Machine Learning Engineer
  Compute currency conversion via graph search

  This question evaluates graph modeling and pathfinding with weighted edges, numerical reasoning about multiplicative rates and floating-point precision, and the ability to analyze algorithmic time and space complexity.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Data Scientist
  Compute maximum concurrent trips from intervals

  This question evaluates a candidate's skill in designing efficient algorithms for interval overlap counting, including event ordering, handling half-open interval edge cases, and reasoning about concurrent activity timing.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Software Engineer
  Compute outer boundary of an N-ary tree

  This question evaluates proficiency in tree data structures and algorithm design, focusing on traversal and boundary extraction in ordered N-ary trees while requiring time and space complexity analysis.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Data Scientist
  Compute square root to 1 decimal

  This question evaluates a candidate's understanding of numerical methods, floating-point precision, and implementation of approximate functions when producing a square root rounded to one decimal place.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Software Engineer
  Compute time for virus to infect grid

  This question tests the ability to model state propagation in a 2D grid using a Breadth-First Search (BFS) approach to determine the minimum time required for a condition to cover all cells.

  Coding & AlgorithmsTechnical ScreenBFSGraphGrid
• UberMedium·Data Scientist
  Convert a PDF to a CDF

  This question evaluates understanding of probability theory, numerical methods, and algorithmic implementation for converting probability density functions into cumulative distribution functions, addressing analytic versus sampled PDFs along with support, normalization, and monotonicity requirements.

  Coding & AlgorithmsTechnical Screen
• UberMedium·Software Engineer
  Deep Equality of Two Records

  This problem tests your ability to recursively compare two complex, nested data structures while handling different types like dictionaries, lists, and primitives.

  Coding & AlgorithmsTechnical ScreenRecursionData StructuresHash Maps
• UberMedium·Data Scientist
  Derive a CDF from a PDF

  This question tests the ability to derive a Cumulative Distribution Function (CDF) from a given Probability Density Function (PDF), ensuring proper integration and boundary conditions.

  Statistics & MathTechnical ScreenProbabilityCalculusStatistics
• UberMedium·Machine Learning Engineer
  Describe an ML system you built

  This question evaluates end-to-end machine learning system design and ownership — covering problem framing, data and feature engineering, model choice, training and evaluation, deployment, monitoring, and measured impact — and it probes interpersonal leadership in handling conflicts over model decisions, metrics, or product trade-offs.

  Behavioral & LeadershipTechnical Screen
• UberHard·Data Scientist
  Design ETA prediction for Uber rides

  This question evaluates competency in end-to-end machine learning system design for real-time spatiotemporal ETA prediction, encompassing feature engineering from heterogeneous data sources, uncertainty quantification, model selection, and production serving and monitoring.

  Machine LearningTechnical Screen
• UberMedium·Data Scientist
  Design Pricing Model Experiment

  This question evaluates experimental design and causal inference competencies for marketplace pricing, covering metric selection, guardrail definition, interference and switchback designs, and sample-size and treatment-window tradeoffs.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Software Engineer
  Design Stock Price Alerts

  This question evaluates a candidate's ability to design scalable, low-latency alerting and notification systems, covering system architecture, APIs, data modeling, event-driven alert evaluation, deduplication, and multi-channel delivery; it is in the System Design category and assesses practical application skills.

  System DesignTechnical Screen
• UberMedium·Software Engineer
  Design a Food-Delivery Backend (Uber Eats-style)

  This problem evaluates your ability to design a distributed system that manages real-time interactions between three distinct user types: customers, restaurants, and couriers, with a focus on geospatial querying and high-concurrency event streaming.

  System DesignTechnical ScreenMicroservicesGeospatialCaching
• UberMedium·Data Scientist
  Design a Ride-Hailing ETA System

  This question evaluates a data scientist's competency in end-to-end machine learning system design for spatiotemporal prediction, covering temporal and geospatial feature engineering, model family selection, metric tradeoffs and calibration, selection bias and noise handling, latency and deployment constraints, monitoring, and experiment design.

  Machine LearningTechnical Screen
• UberMedium·Software Engineer
  Design a Rolling Event Counter

  This question evaluates understanding of data structures and time-windowed counting, performance and memory trade-offs, expiration policies, and concurrency when implementing an in-memory rolling event counter.

  Software Engineering FundamentalsTechnical Screen
• UberMedium·Frontend Engineer
  Design a Scalable Calendar Service

  This problem tests your ability to design a distributed calendar system, focusing on data modeling for recurring events, time zone handling, and frontend state management for a high-concurrency scheduling UI.

  System DesignTechnical ScreenAPIDatabaseFrontend
• UberMedium·Machine Learning Engineer
  Design a feed ranking system

  This question evaluates a candidate's competency in designing end-to-end machine-learning-based feed ranking systems, including setting ranking objectives, candidate generation and filtering, feature design, model training and serving, and operational concerns like freshness, diversity, and scalability.

  ML System DesignTechnical Screen
• UberMedium·Data Scientist
  Design airport dispatch with ETA uncertainty

  This question evaluates proficiency in online algorithm design under uncertainty, stochastic modeling of ETAs, multi-objective optimization for resource allocation, and streaming data handling relevant to dispatch systems.

  Coding & AlgorithmsTechnical Screen
• UberHard·Data Scientist
  Design an A/B test; choose Z vs T

  This question evaluates a candidate's competency in experimental design and statistical inference, covering sample-size and power calculations, the choice between Z and T tests, sequential testing/alpha-spending strategies, and causal-inference planning for non-randomized rollouts.

  Statistics & MathTechnical Screen
• UberHard·Data Scientist
  Design an ETA experiment under interference

  This question evaluates a data scientist's competency in experimental design and causal inference for two-sided marketplaces, focusing on interference, partial compliance, metric definition, randomization strategy, instrumentation, estimation, and power calculations within the Analytics & Experimentation domain.

  Analytics & ExperimentationTechnical Screen
• UberMedium·Software Engineer
  Design an Iterative Path Tree and Clarify File Lookup Semantics

  Design an in-memory directory tree supporting iterative path insertion and file lookup using an explicit stack. The problem tests handling of duplicate component names, memory-efficient tree representation, and clarifying ambiguous file search contracts.

  Software Engineering FundamentalsTechnical ScreenTreeDFSDesign