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Interview questions
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Interview questions
Design metrics resilient to data quality
This question evaluates the ability to design robust data-quality metrics and apply SQL window-function techniques for deduplication and time-windowed completeness calculations, focusing on quantifying how ingestion delays, duplicates, and negative amounts can bias payment metrics.
Interview questions · Gemini
Design offline backtest and online experiment
This question evaluates competencies in fraud detection, transaction labeling, feature engineering, offline backtesting, causal inference for counterfactual estimation, and online experiment design within the Analytics & Experimentation domain for data scientists.
Interview questions · Meta
Design offline segments for Meta Portal retail
This question evaluates a data scientist's competency in analytics and experimentation (Analytics & Experimentation), specifically translating behavioral telemetry into data‑driven retail customer segments, market opportunity sizing, prioritization scoring, and in‑store experimental validation for a plug‑in home video‑calling device.
Interview questions · Meta
Design pre-launch plan and cluster A/B test
This question evaluates a candidate's skills in experimental design, causal inference, cluster randomization, metric definition and instrumentation, power analysis, and phased rollout decision-making.
Interview questions · Robinhood
Design Real-Time Analytics Pipeline with Kafka and Flink
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Design Real-Time Analytics Pipeline with Kafka and Flink states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Shopify
Design robust experiment for ambiguous core change
This question evaluates a data scientist's competencies in experimental design and causal inference, including defining success metrics and guardrails, choosing randomization units under interference, power and sample-size calculations, variance reduction, heterogeneous treatment effect detection, multiple-testing control, and operational data-quality and rollout criteria. It is commonly asked in Analytics & Experimentation interviews because organizations must justify randomized evaluations for features with network effects and operational constraints; the domain is Analytics & Experimentation and the level is primarily practical application grounded in conceptual statistical understanding.
Interview questions · Meta
Design robust group size limiting for calls
This question evaluates distributed systems design, concurrency control, admission-control algorithms, and scalability under real-time and race-condition constraints.
Interview questions · Amazon
Design rolling-window top-K click tracker
Design rolling-window top-K click tracker 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 · Visa
Design rotating warehouse simulator with closures
This question evaluates algorithmic skills in designing efficient dynamic data structures and performing event-driven simulation with mutable state, specifically managing cyclic capacity resets and permanent closures in the Coding & Algorithms domain.
Interview questions · Thumbtack
Design streaming new-vs-returning monthly metrics
This question evaluates a candidate's competency in designing scalable, stateful streaming analytics for monthly NEW vs RETURNING request metrics, focusing on event-time processing with late/out-of-order arrivals, deduplication, compact state and probabilistic data-structure trade-offs with quantifiable error bounds.
Interview questions · Meta
Design structure for top-K frequent elements
This question evaluates a candidate's competency in algorithm and data-structure design, frequency counting and streaming/approximation techniques, and the ability to reason about time/space complexity and workload-driven trade-offs.
Interview questions · Flatiron Health
Design success and guardrail metrics
This question evaluates a data scientist's ability to design rigorous metric frameworks and randomized experiments, covering competencies such as defining primary success and guardrail metrics with attribution rules, randomization and bucketing strategies, SRM and event-loss detection, handling novelty and seasonality, segmentation and multiple-comparison control, and decision rubrics. It is asked in Analytics & Experimentation interviews to assess rigor in causal measurement, trade-off reasoning, and product-metrics literacy, testing both conceptual understanding and practical application of statistical and experimentation principles.
Interview questions · Fannie Mae
Design Test Cases for UI Change: Ensuring Quality and Functionality
Evaluates mission-aware QA collaboration for UI and backend changes in a data-science role. Strong answers compare test layers, define acceptance criteria, cover accessibility and auditability, and design robust test cases.
Interview questions · Epic Systems
Design time strategy for rapid-fire OA
Design time strategy for rapid-fire OA 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.
Interview questions · Uber
Design top-K tracker with linked lists
Design top-K tracker with linked lists 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 · Meta
Design video-ads experiment and handle null results
This question evaluates experimental design, causal inference, statistical power analysis, metric definition, and operational analytics competency within the Analytics & Experimentation domain for a Data Scientist role.
Interview questions · Thumbtack
Detail NLP preprocessing and n‑gram choices
This question evaluates a data scientist's competency in NLP preprocessing and feature engineering, covering modality-specific text normalization, tokenization and subword choices, n-gram selection and sparsity trade-offs, handling of OOV terms/emojis/URLs/code, and empirical validation and model comparison.
Interview questions · Meta
Detect cycles and order with DFS
This question evaluates understanding and implementation of graph traversal concepts such as depth-first search, explicit cycle detection, and topological ordering, and falls within the coding and algorithms domain, requiring practical application alongside conceptual understanding.
Interview questions · Gemini
Detect k events in a sliding window
This question evaluates a candidate's ability to design efficient sliding-window and streaming algorithms for time-series event data, encompassing deduplication, handling bounded out-of-order arrivals and clock skew, distinct-device counting, and meeting specified time and space complexity constraints.
Interview questions · Amazon
Detect words formed by multiple dictionary parts
This question evaluates proficiency in string processing, selection and use of appropriate data structures and algorithms (hashing, tries), dynamic programming and memoization, and complexity analysis for large inputs and edge-case handling within the Coding & Algorithms domain.
Interview questions · Microsoft
Determine dictionary-based string segmentation
This question evaluates string segmentation competency, including understanding of dynamic programming, scalable token-matching techniques (prefix data structures and hashing), and handling large input sizes.
Interview questions · Capital One
Determine Earliest Start Date for Strategic Business Analyst
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Determine Earliest Start Date for Strategic Business Analyst states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Disney
Determine if chasing points will meet
This question evaluates understanding of pursuit curves, continuous dynamical systems, computational geometry, and numerical simulation, measuring the ability to reason about concurrent motion, convergence behavior, and numerical precision when multiple agents chase one another.
Interview questions · Roblox
Determine if players prefer local creators without experiments
This question evaluates a candidate's competency in causal inference and observational study design within product analytics, covering metric selection, unit-of-analysis and sessionization, identification strategies, covariate balancing, instrument selection, robustness checks, and data engineering considerations.