Interview questions

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1. Interview questions · Mercor

   Currency Exchange Rate Between Two Currencies via Chained Conversions

   Given a list of pairwise currency exchange rates such as USD to GBP at 0.77, compute the rate from a source currency to a target currency, converting through intermediate currencies and using rates in reverse where needed. It tests reasoning about chained conversions, unknown or unreachable currencies, and a clear time and space complexity analysis.

2. Interview questions · Brex

   Debug and Explain Fixes

   This question evaluates a candidate's debugging and root-cause analysis skills, familiarity with unit testing and regression prevention, and ability to modify a small codebase to fix defects within the coding and algorithms domain.

3. Interview questions · Headway

   Debug and extend obstacle-run statistics classes

   This question evaluates object-oriented design, debugging, algorithmic reasoning, and probabilistic simulation competencies by requiring fixes to class behavior, implementation of aggregate statistics, and a Monte Carlo-based probability estimator.

4. Interview questions · Scale AI

   Debug ML pipeline and build text parser

   This question evaluates practical machine learning engineering skills, including debugging unfamiliar codebases, parsing noisy text data, and identifying subtle defects such as train/test leakage or shape mismatches. It tests applied Python proficiency and systematic reasoning under time pressure, core competencies assessed in ML engineer technical interviews.

5. Interview questions · Home Depot

   Debug row loss after SQL joins

   This question evaluates SQL data manipulation and JOIN logic skills, including temporal and timezone-aware date handling and aggregation, within the Data Manipulation (SQL/Python) domain and primarily tests practical application-level competency.

6. Interview questions · Meta

   Decide and experiment on Group Call feature

   This question evaluates a data scientist's proficiency in product analytics, causal inference, metric engineering, experimentation design, and post‑launch diagnostics within the Analytics & Experimentation domain.

7. Interview questions · Capital One

   Decide and test Groupon program incrementality

   This question evaluates experimental design, causal inference, uplift and profitability modeling, operational risk assessment, and KPI-driven decision rules within the Analytics & Experimentation domain for data scientists, testing both conceptual understanding and practical application of A/B testing, sample-size considerations, and guardrail metrics. It is commonly asked to determine whether a candidate can translate business constraints into a rigorous experiment and profit-oriented analysis, balancing incremental impact estimation, cannibalization concerns, and operational trade-offs to support go/no-go decisions.

8. Interview questions · Capital One

   Decide content volume and price under uncertainty

   This question evaluates a candidate's competency in pricing and content-production optimization under uncertainty, including defining optimization objectives and constraints, specifying necessary metrics, and assessing sensitivity to changes in variable costs.

9. Interview questions

   Decide goal under ambiguity

   This question evaluates product leadership and data-science competencies, including strategic goal selection under ambiguity, metric design, hypothesis framing, and cross-functional decision-making for a subscription product, and it sits in the Behavioral & Leadership category within the data science/product analytics domain.

10. Interview questions · Chime

    Decide launch with CPA-profit trade-offs by segment

    This question evaluates competency in metric-driven profit analysis, customer economics (LTV/CAC), segmentation effects, and interpretation of A/B experiment results.

11. Interview questions · Amazon

    Decide standardization, sparse numerics, correlated features

    This question evaluates a candidate's competency in feature engineering and preprocessing for mixed-type tabular data, including handling sparse counts and heavy-tailed monetary features, missingness and zero-inflation, correlated continuous measurements, model-specific scaling needs, and the design of leak-safe pipelines and validation strategies.

12. Interview questions · Meta

    Decide under adverse signals and conflicts

    This question evaluates decision-making under uncertainty, risk management and trade-off analysis, cross-functional leadership, and the ability to define pre-commitment, rollback, and communication plans for product launches.

13. Interview questions · Capital One

    Decide whether to invest in R&D

    This question evaluates a data scientist's competency in data-driven decision-making, financial modeling, product strategy, and cross-functional leadership by requiring synthesis of customer insight, NPV and cost analysis, cannibalization effects, operational risks, brand positioning, and a staged rollout plan.

14. Interview questions · Snapchat

    Decide whether to launch Group Story

    This question evaluates a data scientist's competence in experimental design and causal inference, covering metric definition and guardrails, network interference and randomization choices, statistical treatment of heavy-tailed engagement metrics, power and duration planning, incremental lift decomposition for cannibalization, and cohort heterogeneity analysis. It is commonly asked in Analytics & Experimentation interviews because it probes both conceptual understanding of causal and interference issues and practical application of statistical experiment methodology at product-launch scale.

15. Interview questions · Capital One

    Decide whether to partner with Groupon

    This question evaluates a data scientist's skills in business decision-making, unit-economics analysis, pricing strategy, and strategic leadership by requiring explicit numeric thresholds, risk identification, and an executive recommendation for partnering with a third-party voucher marketplace.

16. Interview questions · Metropolis

    Deep-dive a cross-team technical project you led

    This question evaluates leadership, cross-team collaboration, technical ownership, project management, and the ability to make architecture trade-offs, mitigate reliability/security/performance risks, and quantify outcomes.

17. Interview questions · Oracle

    Deep-dive a project and show ownership

    This question evaluates a candidate's ownership and leadership competencies alongside technical depth, cross-team alignment, communication, and the ability to conduct system and design reviews within software engineering.

18. Interview questions · Datadog

    Deep-dive a recent project

    This question evaluates project ownership, technical leadership, system architecture, trade-off reasoning, and the ability to articulate goals, constraints, decisions, metrics and postmortem lessons in a software engineering context.

19. Interview questions · Shopify

    Deep dive a technical project and its impact

    This question evaluates a data scientist's ability to lead and execute end-to-end technical projects, including problem framing, data sourcing and quality, modeling and experimentation, deployment and monitoring, and quantifying business impact.

20. Interview questions · Speak

    Deep dive into a past project

    This question evaluates competence in technical leadership, end-to-end system and architecture design, trade-off analysis, and measurable project outcomes within the Behavioral & Leadership category of software engineering and systems architecture.

21. Interview questions · Roblox

    Defend a metric choice under scrutiny

    This question evaluates a Data Scientist's competency in metric selection, stakeholder communication, and experiment governance when defending a non-obvious primary metric.

22. Interview questions

    Defend fit and handle pressure in finance interview

    This Behavioral & Leadership interview question evaluates a candidate's ability to translate data science experience into FP&A and finance analytics competencies—quantifying prior impact, handling monthly close and variance analysis, revenue forecasting and headcount/opex tracking, leveraging SQL/Excel, and managing stakeholder pressure.

23. Interview questions · xAI

    Define and compare smart pointers

    This question evaluates understanding of memory management and ownership semantics, covering competencies such as RAII, reference counting, lifetime management, custom deleters, and the roles of unique, shared, and weak smart pointer types.

24. Interview questions · Meta

    Define composite success for search and test it

    This question evaluates competency in designing composite success metrics, experiment design, and evaluation pipelines for search features, testing skills in metric formulation, calibration to business outcomes, aggregation strategies, handling missing or correlated labels, statistical power and sample-size reasoning, label collection and monitoring within the Analytics & Experimentation domain for Data Scientist roles. It is commonly asked to assess alignment of measurement with business objectives and the ability to reason about trade-offs and biases, and it tests both conceptual understanding of trade-offs and practical application in running online experiments and building offline evaluation pipelines.