Interview questions

1,290 resources · Page 7 of 54

1. Interview questions · Amazon

   Choose regularization norms and model formulations

   This question evaluates understanding of regularization norms (L0, L1, L2, L∞), objective formulations for linear and logistic regression, and practical competencies in handling collinearity, outliers, feature scaling, and optimization difficulty.

2. Interview questions · Meta

   Choose threshold under asymmetric costs

   This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.

3. Interview questions · First American

   Clarify Ambiguous Requirements and Resolve Team Conflicts

   This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Clarify Ambiguous Requirements and Resolve Team Conflicts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

4. Interview questions · Fanatics

   Clarify employment type at Microsoft

   This question evaluates a candidate's ability to clearly articulate employment arrangements and the degree of ownership, accountability, performance evaluation, and measurable impact on projects, reflecting leadership, communication, and product-stewardship competencies.

5. Interview questions · Amazon

   Clarify internship availability and conversion

   This question evaluates a candidate's clarity about availability, logistical constraints, and intent to convert an internship into a full-time role, testing communication of exact dates, work authorization, relocation, and academic conflicts.

6. Interview questions · Meta

   Clarify scope and align to mission

   This question evaluates a data scientist's product sense, leadership and stakeholder-alignment skills, including scope clarification, mission translation, metric definition, and collaborative communication.

7. Interview questions · Apple

   Clarify status, education, and multi-role strategy

   This question evaluates a candidate's ability to document and communicate U.S. work authorization details, resolve resume inconsistencies, articulate multi-level role positioning, and demonstrate minicase availability and preparation—assessing attention to detail, clarity of communication, stakeholder coordination, and practical readiness for data-science interviews. Commonly asked in Behavioral & Leadership screens for Data Scientist roles to verify eligibility, alignment with role expectations, and logistical readiness, it tests both conceptual understanding of career-leveling and contingency planning and practical application of documentation, messaging, and time-management.

8. Interview questions · Reddit

   Collaborate with PM and Eng as DS

   This question evaluates a data scientist's cross-functional collaboration, prioritization, ownership, stakeholder management, and influencing skills when engaging with product managers and engineers.

9. Interview questions · Reddit

   Communicate and de-risk a non-experimental launch

   This question evaluates a data scientist's competency in interpreting causal-inference results from a Synthetic Control, communicating evidence strength and residual risks to executives and skeptical partners, structuring rollout governance and rollback criteria, and managing bias control, dissent and cross-functional accountability.

10. Interview questions · TikTok

    Communicate technical impact under skeptical stakeholders

    This question evaluates a data scientist's ability to communicate technical impact to skeptical stakeholders and demonstrate causal evidence for improvements, testing competencies in experiment design, causal inference, metrics-driven evaluation, and technical leadership expressed through design choices and risk trade-offs.

11. Interview questions · Meta

    Communicate trade-offs and influence launch

    This question evaluates product judgment, experimentation interpretation, and cross-functional leadership skills for a data scientist, focusing on communicating trade-offs between per-account metric lifts and cross-account cannibalization; Category: Behavioral & Leadership, Domain: data science and product experimentation.

12. Interview questions · Amazon

    Communicating Top-Down Change

    Practice communicating top-down operational change across peer managers, direct-report managers, and front-line employees. The solution covers safety, efficiency, cost goals, audience-specific messaging, pilots, feedback loops, metrics, guardrails, and sustainment.

13. Interview questions · PayPal

    Compare final, finally, finalize

    This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compare final, finally, finalize states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

14. Interview questions · LinkedIn

    Compare heap and stack memory

    This question evaluates understanding of memory management concepts including stack versus heap allocation, object lifetimes, allocation and free behavior, access patterns, performance characteristics, and thread visibility within the Coding & Algorithms domain.

15. Interview questions · Meta

    Compare Instagram vs. Facebook using causal experiments

    This question evaluates causal inference, experimentation design, metric engineering, and ecosystem-level impact assessment skills for a Data Scientist in the Analytics & Experimentation domain, with emphasis on defining a single-objective OEC that prevents pure cannibalization and on measuring cross-app effects via randomized rollouts.

16. Interview questions · Thumbtack

    Compare list/dict; parse JSON/CSV at scale

    This question evaluates understanding of Python data structures (list vs dict), algorithmic time and memory complexity, ordering guarantees in CPython, large-scale parsing and streaming of JSON/CSV, and robust data-cleaning and error-handling strategies.

17. Interview questions

    Compare LLN and CLT with heavy tails

    This question evaluates understanding of probabilistic limit theorems and heavy-tailed behavior, specifically the assumptions and convergence guarantees of the Law of Large Numbers and the Central Limit Theorem when applied to Pareto-like distributions.

18. Interview questions · Apple

    Compare Normal vs Poisson; test dispersion and approximate tails

    This question evaluates a data scientist's competency in statistical inference for count data, covering MLE and asymptotic variance estimation, Wald confidence intervals, tests for equidispersion versus overdispersion, Normal approximation with continuity correction for Poisson tails, and model selection diagnostics.

19. Interview questions · Capital One

    Compare solar vs biomass paybacks and recommend

    This question evaluates quantitative financial modeling, scenario analysis, and decision-making skills for a Data Scientist role by requiring calculation of annual profit and payback periods, incorporation of carbon credits, and comparative output analysis; it tests the Statistics & Math domain and emphasizes practical application of numerical analysis rather than purely conceptual theory. It is commonly asked to gauge proficiency in applying statistical and mathematical techniques to real-world energy investment problems, performing sensitivity and scenario analyses, and integrating non-financial considerations such as supply risk, permitting complexity, and scalability into evidence-based comparisons.

20. Interview questions · Fidelity

    Compare Spring and Spring Boot

    Compare Spring and Spring Boot 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.

21. Interview questions · EY

    Compare two FS domains deeply

    This question evaluates a data scientist's ability to analyze and contrast data modeling, control frameworks, and operational failure modes across two financial services domains, measuring competency in domain knowledge, data governance, and system reliability.

22. Interview questions · Optiver

    Compare two programs for equivalence

    This question evaluates program-equivalence reasoning skills, including understanding of language semantics (mutation, ordering stability, integer overflow/underflow, integer-division behavior), competency in comparing time and space complexity, and the ability to design property-based tests and edge cases.

23. Interview questions · PayPal

    Compare WHERE vs HAVING with aggregates

    Compare WHERE vs HAVING with aggregates evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

24. Interview questions · Goldman Sachs

    Complete decision tree and gradient descent functions

    This question evaluates implementation skills in supervised machine learning and numerical optimization, specifically computing Gini impurity and selecting optimal binary splits for a decision tree alongside deriving and applying a single-step gradient descent update for linear regression.