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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 · NVIDIA
Parse a deeply nested JSON
This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Parse a deeply nested JSON states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · OpenAI
Parse and build binary data in Python
This question evaluates proficiency in binary data handling and serialization/deserialization in Python, including little-endian integer encoding, length-prefixed payloads, robust error handling for short reads and invalid lengths, and testability and performance considerations.
Interview questions · Cortex
Parse API JSON and print org chart
This question evaluates HTTP and JSON parsing, CSV processing, hierarchical data modeling, sorting, and algorithmic efficiency for building and printing an organizational chart.
Interview questions · Mithril
Parse code into operation triads IR
This question evaluates parsing and IR-generation skills, including tokenization, AST or three-address (operation triads) construction, deterministic control-flow serialization, and management of temporaries and labels.
Interview questions · Two Sigma
Perform no-intercept linear regression from two datasets
This question evaluates linear regression modeling, numerical linear algebra (normal equations and stable factorization methods), regularization, R² interpretation, and data-preprocessing/validation skills within the Machine Learning domain for a Data Scientist role.
Interview questions · TikTok
Personal Motivation and Cultural Fit
Practice answering TikTok behavioral PM questions about cross-cultural product insight, project selection, motivation, and relocation. The guide shows how to turn US-China experience into product decisions, define project trade-offs, explain interest in ByteDance or TikTok, and answer mobility questions clearly.
Interview questions · Meta
Persuade engineers to launch pinned-unread chats
This question evaluates persuasion, product sense, metrics-driven reasoning, and technical trade-off analysis for a Data Scientist within a Behavioral & Leadership domain, requiring clear problem definition, baseline metrics, impact estimation, and consideration of engineering constraints like ranking complexity, real-time updates, caching, client performance, edge cases, and accessibility. It is commonly asked to assess the ability to influence engineering stakeholders, design incremental rollouts with success metrics and guardrails, and balance high-level conceptual strategy with practical implementation planning (both conceptual understanding and practical application).
Interview questions · Instacart
Pivot order-status counts
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Pivot order-status counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Meta
Pivot Projects Quickly and Foster Team Inclusion
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Pivot Projects Quickly and Foster Team Inclusion states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Stripe
Plan an experiment to validate targeting impact
This question evaluates a candidate's competency in experimental design, causal inference, statistical power and sample-size calculations, contamination and attribution handling, and cost–benefit analysis for targeted marketing experiments.
Interview questions · LinkedIn
Plan and lead a large recommendation project
This Behavioral & Leadership question evaluates leadership, execution planning, cross-functional coordination, resourcing, and stakeholder communication skills in the context of machine learning recommendation product development.
Interview questions · Meta
Prepare answers for core behavioral questions
This question set evaluates interpersonal and leadership competencies such as communication, conflict resolution, giving and receiving feedback, problem-solving, ownership, handling ambiguity, and the ability to articulate measurable impact and self-awareness.
Interview questions · Ubs
Prepare responses to UBS behavioral and stock-pitch prompts
Prepare responses to UBS behavioral and stock-pitch prompts 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 · Thumbtack
Present a DS project with business impact
This question evaluates presentation and leadership competencies in data science—specifically the ability to communicate analytical approach, quantify business impact, explain assumptions and validation, and justify trade-offs to a mixed technical audience.
Interview questions · Plaid
Present a project deep dive
This Behavioral & Leadership question evaluates ownership, technical leadership, communication, system architecture, data modeling, and operational competency in software engineering by requesting an end-to-end project walkthrough that covers goals, stakeholders, architecture, trade-offs, testing, metrics, incidents, and lessons.
Interview questions · OneMain Financial
Present a project to non-technical leaders
This question evaluates a data scientist's ability to communicate a modeling project's business framing, metric choices, trade-offs, risk mitigation, and quantified impact while handling cross-functional questions.
Interview questions · Robinhood
Present a Project With Correctness Guarantees
This question evaluates a candidate's system design, reliability engineering, and ownership skills by requiring explanation of correctness guarantees such as retry handling, duplicate request management, partial-failure mitigation, idempotency, and validation of production behavior.
Interview questions · Cloudflare
Present a recent project effectively
This question evaluates communication, leadership, and ownership skills within the Behavioral & Leadership domain by assessing how clearly a software engineer describes a recent project and their individual contributions.
Interview questions · Snowflake
Present an end-to-end project and defend decisions
This question evaluates a data scientist's end-to-end project leadership, product and experiment design, metric selection, trade-off analysis, and cross-functional stakeholder negotiation.
Interview questions · Capital One
Present Analytical Process and Recommendations for Credit-Card Profitability.
Evaluates executive communication of credit-card profitability and partnership analysis. Strong answers lead with recommendation, show assumptions and calculations, bring clear artifacts, summarize risks, and drive next steps.
Interview questions · Shopify
Present pirated-usage findings to a PM
This question evaluates communication and leadership competencies in a data science context, specifically data storytelling, stakeholder-facing presentation of analytical findings, revenue-impact estimation, metric selection and interpretation, and data-quality assessment.
Interview questions · Anthropic
Present project and answer behaviorals
This question evaluates leadership and behavioral competencies alongside technical communication skills, including project ownership, system architecture articulation, trade-off reasoning, metrics-driven impact measurement, stakeholder management, and mentorship.
Interview questions · OneMain Financial
Present Successful Analytics Project: From Problem to Impact
Evaluates end-to-end analytics project storytelling for an onsite panel presentation. Strong answers cover business context, data, methodology, validation, impact, deployment, stakeholder feedback, and lessons learned.
Interview questions · Capital One
Prevent data registration outage and reduce loss
This question evaluates competency in production data reliability, monitoring and alerting, incident response, reconciliation, and cross-functional leadership for analytics pipelines.