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Yahoo interview questions
8 practice questions in the style of Yahoo's interviews, covering Statistics & Math, Data Manipulation (SQL/Python), Analytics & Experimentation, Machine Learning, Coding & Algorithms.
3 easy5 medium0 hard
Yahoo interview questions by the numbers
Last updated
Katkat has 8 Yahoo interview questions. The largest topics are Coding & Algorithms (38%), Statistics & Math (25%) and Analytics & Experimentation (13%). By difficulty: 38% easy and 63% medium. The most common round is Technical Screen (100%).
Topics
- Coding & Algorithms38% · 3
- Statistics & Math25% · 2
- Analytics & Experimentation13% · 1
- Machine Learning13% · 1
Difficulty
- Easy38% · 3
- Medium63% · 5
Interview rounds
- Technical Screen100% · 8
Roles
- Data Scientist75% · 6
- Machine Learning Engineer25% · 2
Showing 1–8 of 8 results
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Flips for Heads-Tails Sequence states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
YahooData ScientistStatistics & MathTechnical ScreenThis question evaluates a data scientist's proficiency in transactional data manipulation using SQL and Python, focusing on aggregation, ranking, joins, date arithmetic, and numeric rounding to produce user-level and time-series metrics.
YahooData ScientistData Manipulation (SQL/Python)Technical ScreenThis interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Evaluate Widget Impact on User Engagement with A/B Testing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
YahooData ScientistAnalytics & ExperimentationTechnical ScreenA machine learning fundamentals round covering overfitting and underfitting, the bias-variance trade-off, class imbalance, and missing data. It tests whether a candidate can define each concept precisely, diagnose it from training and validation evidence, and choose remedies while weighing their side effects.
YahooMachine Learning EngineerMachine LearningTechnical ScreenThis question evaluates graph-theoretic reasoning and string-manipulation skills, focusing on the ability to reason about ordering constraints, edge cases like self-loops and multiple identical relationships, and traversal over relationships between items.
YahooData ScientistCoding & AlgorithmsTechnical ScreenThis interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Prove Equal Probability Impossible with Relabeled Dice Faces states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
YahooData ScientistStatistics & MathTechnical ScreenRank candidate items for a user by the dot product of the user's embedding with each item's embedding, returning item indices from highest to lowest score with ties broken by index. A follow-up removes items the user has already seen, testing vectorized scoring, sorting and filtering.
YahooMachine Learning EngineerCoding & AlgorithmsTechnical ScreenThis question evaluates proficiency in fundamental data structure manipulation and string-processing algorithms, specifically preserving insertion order during list deduplication and performing iterative overlap-based word merging.
YahooData ScientistCoding & AlgorithmsTechnical Screen
Yahoo interview experiences
All 2 →- 60-Minute Screen With a Numpy Embedding Ranking QuestionYahoo · Machine Learning Engineer · Technical Screen · Aug 2026
60 minutes, coding + ML knowledge. The coding had to be done with numpy. The input was a user embedding vector and N item embedding vectors. Taking the dot product of the two gives a similarity score. The task was to sort the N items by score from high to low, and the output is the list of item indices after sorting. The follow-up: if the user has already seen some of those items, filter the seen…
- Great Technical Screen, Then the Role Got PausedYahoo · Machine Learning Engineer · Senior+ · Technical Screen · Feb 2026
Yahoo News interview (April), Round 1. Question 1: Talk about your background and one of your projects in detail — they dug deep into one particular project. Key points for the answer (aim for a total of 3–6 minutes) and my own suggestions: Quick background (20–30s): one sentence on the role, your responsibilities on the team, and list 1–2 projects. Deep-dive framework (3–4 minutes, using STAR):…
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