practice a little every day, paws crossed ♡
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 1441–1470 of 4616 results
- DoorDashMediumSoftware EngineerDetermine equality after limited swaps
This question evaluates string manipulation and combinatorial reasoning about character swaps and anagram formation, focusing on competencies such as frequency analysis and reasoning about minimal edit operations.
Coding & AlgorithmsTechnical Screen - DoorDashMediumData ScientistDiagnose Cold-Food Deliveries and Make a Launch Decision
Work through a product analytics case about diagnosing a delivery-quality problem and deciding whether an intervention should launch. The question tests metric hierarchy, causal reasoning, investigation design, power considerations, and decision criteria.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistDiagnose Decline in Successful Orders
This question evaluates a data scientist's skills in marketplace analytics, causal reasoning, metric framework design, and experimentation for diagnosing a decline in successful orders.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistDiagnose LA completed-order drop and design experiment
This question evaluates a data scientist's competency in funnel-based diagnostics, causal inference, and experimental design for distinguishing supply-side versus demand-side drivers in a two-sided marketplace, emphasizing metric selection, segmentation, and guardrail monitoring.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistDiagnose a One-Day Drop in Successful Deliveries
Tests the candidate's structured metric troubleshooting, funnel decomposition, and root cause analysis for an acute drop in core marketplace volume. Evaluates the ability to separate count effects from conversion rate changes and isolate partner-specific vs. market-wide anomalies.
Analytics & ExperimentationTechnical Screenmetric-troubleshootingfunnel-analysismarketplace-analytics - DoorDashHardData ScientistDiagnose and experiment to reduce late deliveries
This question evaluates a data scientist's competencies in causal inference, metrics engineering, data instrumentation and joining, root‑cause analysis, and experiment design focused on operational delivery performance.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistDiagnose and reduce cold-food refund costs
This problem tests the ability to diagnose root causes of operational cost drivers and design a rigorous experiment to validate potential mitigation policies.
Analytics & ExperimentationTechnical ScreenCausal InferenceExperimentationMarketplace Analytics - DoorDashHardMachine Learning EngineerDiscuss ML infrastructure fundamentals
Discuss ML infrastructure fundamentals evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - DoorDashMediumSoftware EngineerDiscuss Projects and Planning Improvements
This question evaluates ownership, product understanding, end-to-end project leadership, cross-functional collaboration, and the ability to quantify impact within behavioral and leadership competencies for software engineers.
Behavioral & LeadershipTechnical Screen - DoorDashMediumSoftware EngineerDiscuss feedback, help, and conflict
This question evaluates interpersonal and leadership competencies such as receiving and acting on constructive feedback, mentoring or helping colleagues, and managing conflict within engineering teams (Behavioral & Leadership).
Behavioral & LeadershipTechnical Screen - DoorDashMediumData ScientistEvaluate a Bike Dasher Program with a Controlled Experiment
Tests marketplace experimentation design, tackling spatial-temporal spillover effects, metric selection, and decision criteria for a multimodal courier fleet.
Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace ExperimentationCausal Inference - DoorDashMediumData ScientistEvaluate a Bike-Delivery Pilot with Marketplace Metrics
This problem tests the ability to design an A/B test for a logistics marketplace, focusing on metric definition, experiment randomization strategy, and identifying potential confounding variables.
Analytics & ExperimentationTechnical ScreenA/B TestingMarketplaceLogistics - DoorDashMediumSoftware EngineerFilter Open Restaurants Through Existing Domain APIs
Implement an orchestrator service that coordinates calls to a directory API and an operating-hours API with bounded concurrency, short-circuit filtering, resilient error handling, and in-memory caching.
Software Engineering FundamentalsTechnical Screenconcurrencycachingsystem-integration - DoorDashEasyData ScientistFind Monthly Top Customers Excluding High-Frequency Users
This problem tests proficiency in window functions, filtering based on aggregated metrics, and handling ranking logic in SQL.
Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsData Aggregation - DoorDashMediumSoftware EngineerFind names similar by one swap
This question evaluates string manipulation, character-frequency analysis, and algorithmic reasoning to determine near-matches under a single-character-swap constraint.
Coding & AlgorithmsTechnical Screen - DoorDashMediumData ScientistGenerate Weekly Revenue and Engagement Summary with Pandas
This question evaluates proficiency in data manipulation and analytics with Pandas and SQL, covering aggregations, distinct purchase counts, lambda-based user-tier classification, and platform-level conversion-rate computation.
Data Manipulation (SQL/Python)Technical Screen - DoorDashHardSoftware EngineerHandle a payment-service incident with resource spikes
Handle a payment-service incident with resource spikes 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 & LeadershipTechnical Screen - DoorDashMediumSoftware EngineerHandling Duplicate Upstream Order Records in Dasher Pay Calculation
Explain how a Dasher pay calculation driven by upstream order records stays correct when those records contain duplicates. Tests deciding when two records describe the same fact, making processing safe against replayed records, handling conflicting and late data or corrections after payout, and preventing double payouts.
System DesignTechnical Screen - DoorDashEasyData ScientistHow would you test a bike delivery option?
This problem tests the ability to design an A/B test, define success metrics, and account for confounding variables when introducing a new logistics channel (bike delivery) to a marketplace.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsMarketplace Analytics - DoorDashMediumData ScientistIdentify Challenges and Solutions for Bike-Delivery Program
Evaluates operational and strategic thinking for a bike-delivery courier program. Strong answers identify marketplace, safety, weather, regulatory, courier, merchant, customer-experience, and unit-economics risks, then pair each challenge with practical mitigations and measurable rollout guardrails.
Behavioral & LeadershipTechnical Screen - DoorDashHardData ScientistIdentify Key Drivers of Delivery Decline in Los Angeles
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 Identify Key Drivers of Delivery Decline in Los Angeles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - DoorDashMediumSoftware EngineerImplement a hierarchical key-value store
Implement a hierarchical key-value store evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - DoorDashMediumSoftware EngineerImplement consistent hashing ring
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Implement consistent hashing ring states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - DoorDashMediumSoftware EngineerImplement dasher active-time calculator
This question evaluates competency in time-based event processing, pairing related events, computing durations using LocalDateTime and Duration APIs, and analyzing algorithmic time and space complexity.
Coding & AlgorithmsTechnical Screen - DoorDashMediumSoftware EngineerImplement string matching with follow-up
A DoorDash software-engineer coding screen on string matching: implement firstIndex(T, P) in O(|T| + |P|) time and O(|P|) space using KMP, then handle follow-ups on naive vs KMP vs Rabin–Karp, Unicode and very long inputs, edge cases, wildcard ('?'/'*') matching, and simultaneous multi-pattern search via Aho–Corasick. Includes corrected, complete solutions and complexity trade-offs.
Coding & AlgorithmsTechnical Screen - DoorDashMediumSoftware EngineerImplement string-path file system operations
This question evaluates competency in designing data structures for hierarchical string-path file systems, including string parsing, stateful API semantics, validation logic, and efficient lookup/update operations.
Coding & AlgorithmsTechnical Screen - DoorDashMediumData ScientistInvestigate Causes of Cold Food Deliveries and Solutions
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 Investigate Causes of Cold Food Deliveries and Solutions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistInvestigate Causes of Cold Meal Deliveries
DoorDash data scientist case on cold meal deliveries, covering lifecycle timestamps, complaint metrics, segmentation, root-cause analysis, dispatch and packaging fixes, A/B testing, and marketplace guardrails.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistInvestigate Causes of Increased Driver Wait Time
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 Investigate Causes of Increased Driver Wait Time states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistInvestigate Declining Successful Orders
This question tests your ability to decompose a high-level drop in a key business metric into granular segments and formulate testable hypotheses to explain the underlying root causes.
Analytics & ExperimentationTechnical ScreenProduct AnalyticsFunnel AnalysisHypothesis Testing
DoorDash