Interview questions

1,290 resources · Page 52 of 54

1. Interview questions · Boston Consulting Group

   Summarize impact and lessons from your resume

   This question evaluates a data scientist's ability to communicate measurable impact, design and track metrics, manage stakeholders and conflicts, and demonstrate accountability through concrete project examples, testing Behavioral & Leadership competencies specific to the Data Scientist role.

2. Interview questions · Tinder

   Summarize Relevant Experience

   Prepare a Tinder Profiles PM background answer that connects past product work to self-expression, onboarding, trust, profile quality, collaboration, feedback, and meaningful connections. The solution uses a 90-second narrative with concrete examples and learning moments.

3. Interview questions · Instacart

   Summarize your background concisely

   This question evaluates concise career storytelling, impact-focused communication, technical credibility, and role fit for a Data Scientist, testing behavioral leadership competencies and domain knowledge in data science and analytics.

4. Interview questions · Meta

   Tackle Python tasks under time pressure

   This question evaluates practical Python coding skills, the ability to pose clarifying questions, enumerate and reason about edge cases, produce clean readable code, and summarize time and space complexity along with a minimal test plan within a timed data engineering exercise.

5. Interview questions · Intersystems

   Teach a networking concept to a beginner

   This question evaluates a candidate's ability to teach and explain a networking concept, combining networking subject-matter knowledge with communication, mentorship, and leadership competencies.

6. Interview questions · Uber

   Tell about a past project and impact

   Tell about a past project and impact 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.

7. Interview questions · Meta

   Tell me about a high-impact end-to-end project

   This Meta Product Analyst onsite behavioral question asks you to walk through a high-impact project you drove end-to-end. It evaluates ownership, analytical rigor, metric definition and tracking, cross-functional influence, trade-off judgment, execution, and reflection. The answer uses a STAR + Reflection structure with explicit analytics and quantified outcomes.

8. Interview questions · Amazon

   Tell me about exceeding your responsibility

   Prepare Amazon-style behavioral answers about going beyond formal responsibilities and making a risky deadline decision. Includes STAR structure, sample stories, risk mitigation, and common pitfalls.

9. Interview questions · Walmart Labs

   Tell me about leadership challenges

   Prepare Product Manager internship behavioral answers for self-introduction, launching a project, changing requirements, failure, conflict, and competing priorities. The solution uses STAR examples, reusable story preparation, measurable impact, and lessons learned.

10. Interview questions · Snapchat

    Tell me about leading through conflict

    Prepare a TPM onsite behavioral answer about leading a project end to end, handling a difficult stakeholder, and explaining a counterintuitive leadership lesson with STAR structure and metrics.

11. Interview questions · ByteDance

    Tell me about your fit

    Prepare a TikTok PM career-story answer covering self-introduction, role fit, Company A ownership, project deep dive, job changes, moving to the United States, long-term PM goals, relocation flexibility, metrics, and ecosystem product judgment.

12. Interview questions · Visa

    Tell me about yourself and trade-offs

    Prepare Visa PM recruiter answers for self-introduction, why Visa, and stakeholder tradeoffs. The solution uses Present-Past-Future and STAR structures, emphasizing payments scale, trust, compliance, risk, conversion, and cross-functional product judgment.

13. Interview questions · Hudson River Trading

    Test easy–medium array/string tasks

    This question evaluates a candidate's ability to design concise, robust test cases and translate edge-case reasoning into concrete inputs and expected outputs for array and string problems, reflecting skills in input validation, correctness assurance, and handling varied data characteristics.

14. Interview questions · Meta

    Test two models' proportions for significance

    This question evaluates proficiency in statistical inference for proportions, covering hypothesis testing, confidence intervals, power and sample-size calculations, multiple-testing correction, and comparison of frequentist versus Bayesian approaches within the Statistics & Math domain for data scientist roles.

15. Interview questions · Adobe

    Transform an Array into Its Next Permutation

    Transform an integer array in place into its next lexicographically greater permutation, or wrap to the smallest permutation when none exists. Preserve constant auxiliary space and handle duplicates, empty input, and single elements.

16. Interview questions · Boston Consulting Group

    Transform and aggregate messy event data

    This question evaluates a candidate's competency in data wrangling with pandas, including datetime parsing and timezone considerations, currency/string cleaning and coercion to numeric types with handling of missing or malformed amounts, DataFrame column alignment and concatenation, joins, and group-by aggregations to produce per-country/plan day-level metrics. It is commonly asked to assess practical data-manipulation skills for producing analysis-ready metrics from messy event and user datasets, falls under the Data Manipulation (SQL/Python) domain, and targets practical application rather than purely conceptual understanding.

17. Interview questions · Amazon

    Transform Customer Feedback into Valuable Product Enhancements

    Transform Customer Feedback into Valuable Product Enhancements 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.

18. Interview questions · Meta

    Traverse binary tree by levels with constraints

    This question evaluates binary tree traversal techniques, use of stacks as constrained data structures, predicate-based filtering of nodes, and the ability to argue algorithmic correctness and analyze time and space complexity.

19. Interview questions · Capital One

    Use data to resolve an ambiguous problem

    This question evaluates a data scientist's competency in end-to-end data analysis, causal identification, statistical modeling, data quality and governance, bias assessment, and communication of uncertainty when addressing an ambiguous business problem.

20. Interview questions · Salesforce

    Validate a Generalized Word Abbreviation

    Validate whether a lowercase word matches a generalized abbreviation whose positive numbers skip characters. Enforce exact letter matching, full consumption, multi-digit counts, and the prohibition on leading zeroes.

21. Interview questions · Applied Intuition

    Validate Nested Configuration Objects

    This question evaluates schema validation skills including recursive data structures, primitive and composite type checking, and structured error reporting for configuration objects, categorized under Coding & Algorithms for a Machine Learning Engineer role and focused on practical implementation.

22. Interview questions · Capital One

    Validate Unit-Test Coverage and Identify Missing Scenarios

    This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Validate Unit-Test Coverage and Identify Missing Scenarios states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

23. Interview questions · ByteDance

    Walk through a data pipeline project

    This question evaluates end-to-end data engineering and leadership competencies, including pipeline architecture, data modeling, ingestion and transformation choices, data quality and reliability practices, operational monitoring and SLA considerations, and stakeholder orientation.

24. Interview questions · OneMain Financial

    Walk through a DS project end-to-end

    This question evaluates end-to-end data science competencies including problem framing, metric design, data sourcing and quality assessment, modeling and evaluation choices, risk identification, impact quantification, and stakeholder communication within the Behavioral & Leadership category for a Data Scientist position.