Google·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

A technical phone screen for a Data Scientist role at Google, focused on a machine learning classification problem. The question was well-scoped but had some depth to it once you got past the surface-level setup.

Questions Asked (1)

Given a labeled dataset of 1,000 URLs marked as good or bad (dead links), walk through how you'd build and evaluate a classifier. Why might AUROC be a better evaluation metric than accuracy when the classes are imbalanced?

Product Analytics & MetricsTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I started with logistic regression which felt right, and the interviewer seemed fine with that.

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AI HintsAI Generated

Suggested Approach

Start by outlining the end-to-end ML pipeline: data preparation, feature engineering, model selection, training, and evaluation. Emphasize the importance of choosing appropriate metrics for imbalanced data, explaining why AUROC is preferred over accuracy. Conclude with a discussion on threshold selection and business impact.

Pro tip: Mention that in practice, you'd also consider precision-recall curves and the cost of false positives vs. false negatives, as AUROC can be optimistic when the positive class is rare. This shows you understand the nuances beyond textbook metrics.

1. Data Preparation and Exploration

Split the dataset into training, validation, and test sets, ensuring stratification to preserve class distribution. Explore class balance and check for missing values or anomalies.

2. Feature Engineering

Extract relevant features from URLs, such as length, presence of keywords, domain age, path depth, and query parameters. Consider using TF-IDF on URL tokens or embeddings.

3. Model Selection and Training

Choose a baseline model (e.g., logistic regression) and more complex models (e.g., random forest, gradient boosting). Train using cross-validation and tune hyperparameters.

4. Evaluation Metrics

Use AUROC as the primary metric due to class imbalance, but also report precision, recall, F1, and PR-AUC. Explain that accuracy is misleading because a trivial classifier predicting the majority class can achieve high accuracy.

5. Threshold Selection and Business Impact

Determine the optimal threshold based on the business cost of false positives and false negatives. Discuss how the chosen threshold affects precision and recall and align with stakeholder goals.

Key Points to Mention

Class imbalance: accuracy is not informative when one class dominates; a model predicting all majority class can have high accuracy but be useless.
AUROC measures the model's ability to rank positive instances higher than negative ones, independent of threshold and class distribution.
Precision-recall trade-off and the importance of choosing the right metric based on business objectives.
Cross-validation and stratified sampling to ensure robust evaluation.
Feature engineering specific to URLs, such as lexical features and domain-based features.
Potential pitfalls of AUROC when the positive class is very rare, and the complementary use of PR-AUC.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.