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Interview questions
Explore algorithm tutorials, coding and system design questions, interview experiences, and computer science study guides.
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Interview questions · Instacart
Design a versioned key-value store
Design a versioned 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.
Interview questions · Uber
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.
Interview questions · CloudKitchens
Design an in-memory cloud storage system
This question evaluates data modeling, stateful system design, and algorithmic reasoning for enforcing invariants such as a global namespace, single ownership, and per-user storage quotas.
Interview questions · NVIDIA
Design an IR for test workflows
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Design an IR for test workflows states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Meta
Design analysis to test social vs game engagement
A Meta (Oculus) data science onsite question on designing a rigorous study to test whether social-feature users are more regularly engaged than game-feature users. It covers metric definition, a randomized experiment, an observational causal-inference fallback (PSM/IPW), a two-proportion power calculation, estimation and inference, robustness/bias checks, and a decision rule.
Interview questions · Fetch Rewards
Design analysis to validate reported decline
This question evaluates analytical and experimental-design competencies including metric specification, time-series and same-store comparisons, bias and confounder detection, and concise data-driven communication under a tight time constraint.
Interview questions · Meta
Design and analyze A/B test with interference
This question evaluates proficiency in experimental design and causal inference for online A/B testing under interference, covering competencies such as defining estimands and exposure models, handling clustered or dependent data, variance estimation and confidence intervals, power and sample-size calculations, sequential testing, and diagnostics for leakage. It is commonly asked in Analytics & Experimentation interviews because it probes practical application of statistical design and analysis in production-constrained settings, examines understanding of bias introduced by cross-group interference and operational constraints, and falls within the Analytics & Experimentation domain with a primary emphasis on practical application complemented by conceptual understanding.
Interview questions · Meta
Design and analyze a group-calls experiment
This question evaluates competency in experimental design, causal inference, metric specification, interference handling, and infrastructure-aware analytics within the Analytics & Experimentation domain for a Data Scientist role.
Interview questions · Pinterest
Design and assess a video-pin increase experiment
A Pinterest Data Scientist technical-screen question on Analytics & Experimentation: design a rigorous A/B test for increasing video-pin share in the Home Feed — unit of randomization, primary metric and guardrails, power/MDE, sequential analysis, and a quasi-experiment fallback — then interpret two 14-day readouts and recommend ship, iterate, or stop. It rewards balancing engagement lifts (CTR) against guardrail regressions (retention, complaints, crash rate), multiple-testing robustness, and cross-unit (per-impression vs per-session) trade-offs.
Interview questions · Boston Consulting Group
Design and sample for credit default prediction
This question evaluates a data scientist's competencies in imbalanced binary classification, temporal label definition and leakage detection, sampling and calibration under prior-probability shift, metric and operating-point selection, cost-sensitive decision rules, time-based validation and monitoring, and fairness and operational guardrails.
Interview questions · NVIDIA
Design and secure a REST inference API
This question evaluates a candidate's ability to design secure, scalable REST APIs for machine learning inference, emphasizing API surface design, synchronous and asynchronous workflows, data validation, security controls, and operational concerns like idempotency and batching.
Interview questions · Meta
Design and validate ad model launch
This question evaluates competence in experimental design, causal inference, online A/B testing and launch decision frameworks for ad recommendation systems, covering randomization/unit choice, metric definition and guardrails, power and duration estimation, validity checks, monitoring and visualization in the Analytics & Experimentation domain.
Interview questions · Meta
Design B2C chatbot success metrics and test plan
This question evaluates a data scientist's ability to design product success metrics and a rigorous experiment plan, covering KPI formulation, guardrail metrics and thresholds, randomization and experiment design, logging instrumentation, and quasi-experimental causal inference.
Interview questions · Capital One
Design cloud product packaging and pricing strategy
This question evaluates a candidate's product strategy and quantitative business competencies, including pricing and packaging design, unit economics, risk assessment, and the ability to communicate trade-offs to executives.
Interview questions · Hudson River Trading
Design comprehensive OA test cases
This question assesses a software engineer's practical test design skills under time pressure, specifically the ability to partition input spaces into equivalence classes and boundary cases for algorithm and data structure problems. It evaluates systematic testing methodology, including oracle-based verification strategies and defensible stopping rules — competencies commonly probed in online assessments and technical interviews to gauge engineering rigor.
Interview questions · Thumbtack
Design cross-validation; explain bias–variance
This question evaluates understanding of cross-validation methodologies and the bias–variance tradeoff, testing competency in robust model evaluation, experimental design for temporally ordered and class-imbalanced datasets, and quantitative interpretation of cross-validation error curves.
Interview questions · Lyft
Design experiments for marketplace balance
This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, statistical power calculation, and operational rollout planning for two-sided marketplaces, and it is commonly asked to assess the ability to account for interference, supply constraints, spillovers, and contamination when measuring treatment effects. Categorized under Analytics & Experimentation, it tests both conceptual understanding and practical application by probing exposure and eligibility definitions, metric and guardrail specification, sample-size and clustering adjustments, recognition of heterogeneous treatment effects and sequential monitoring concerns, variance-reduction considerations, and rollout and fallback decision-making.
Interview questions · Anthropic
Design file deduplication at scale
This question evaluates understanding of file-system algorithms, hashing and deduplication techniques, scalable I/O and memory management, and correctness considerations for hash collisions, and it belongs to the Coding & Algorithms domain.
Interview questions · Capital One
Design fundraising experiment and guardrails
This question evaluates experimental design, statistical power and sample-size calculation, selection of primary and guardrail metrics, segmentation with multiple-testing control or hierarchical modeling, stopping rules for sequential analysis, and revenue-impact calculations for A/B testing of email solicitations.
Interview questions · OpenAI
Design GPU credit allocator
A multi-tenant GPU credit allocation system design (OpenAI software-engineer system-design screen): track and deduct GPU credits in real time across many nodes, issue/transfer/spend credits via idempotent APIs, and enforce budgets, hierarchical quotas, rate limits, and fair-share with scheduler integration. Covers prepaid vs postpaid, an immutable double-entry ledger with reservations, overspend prevention, failure recovery, scaling, and observability.
Interview questions · Capital One
Design late-tolerant streaming window aggregator
This question evaluates a candidate's ability to design scalable streaming aggregations and stateful algorithms for handling out-of-order and late-arriving events, including unique-user counting and quantile estimation over time windows.
Interview questions · Meta
Design LRU cache and pick k closest points
Design LRU cache and pick k closest points 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.
Interview questions
Design MapReduce and Spark jobs
This question evaluates proficiency in designing and optimizing distributed data processing jobs, covering Hadoop MapReduce and Spark concepts such as HDFS replication, task re-execution, shuffling/sorting, mapper and reducer key–value semantics, RDD immutability and lineage-based recovery, partitioning, and combiner usage.
Interview questions · Capital One
Design metrics and an A/B test for an app
This question evaluates product analytics and experimentation competencies—product framing, unit economics, revenue modeling, computation-ready metric design, statistical power/sample-size calculation, A/B test setup, and cost-benefit/breakeven analysis—for a Data Scientist role in the Analytics & Experimentation domain, and is commonly asked to assess the ability to translate product hypotheses into measurable business outcomes and defend decisions under stakeholder scrutiny. It tests both conceptual understanding of segmentation, metric validity, and bias considerations and practical application in defining precise numerators/denominators, computing minimum sample sizes and test duration, and producing actionable ROI models.