Coinbase·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Coinbase data scientist interview that went deep on ML fundamentals. They had me implement K-Means from scratch and wanted the full picture, not just the happy path.

Questions Asked (1)

Implement K-Means clustering from scratch without using any ML libraries. Walk through centroid initialization, the assignment step, the update step, convergence, and how you'd handle edge cases like empty clusters.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This was more involved than I expected for a phone screen.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and assumptions, then outline the K-Means algorithm step by step, covering initialization, assignment, update, and convergence. Emphasize how you would handle edge cases like empty clusters and discuss trade-offs such as initialization methods and distance metrics. Conclude with complexity analysis and potential improvements.

Pro tip: Mention K-Means++ initialization and explain why it reduces the chance of poor convergence, showing you understand practical improvements beyond the basic algorithm.

1. Clarify requirements and assumptions

Ask about data size, dimensionality, distance metric, and whether the number of clusters k is given. State assumptions like Euclidean distance and numeric data.

2. Describe initialization

Explain how to initialize centroids, e.g., random selection from data points or K-Means++ for better spread. Mention the impact on convergence and final results.

3. Explain assignment and update steps

Detail how each point is assigned to the nearest centroid, then how centroids are recomputed as the mean of assigned points. Emphasize iterative nature.

4. Discuss convergence and stopping criteria

Define convergence as when assignments no longer change or centroid movement is below a threshold. Mention maximum iterations as a safeguard.

5. Address edge cases and trade-offs

Handle empty clusters by reassigning to farthest point or reinitializing. Discuss computational complexity O(n*k*d*t) and scalability concerns.

Key Points to Mention

K-Means++ initialization for better centroid seeding
Distance metric choice (e.g., Euclidean) and its implications
Empty cluster handling: reassign to farthest point or reinitialize
Convergence criteria: no assignment changes or small centroid shift
Time complexity: O(n*k*d*t) per iteration and scalability
Trade-offs: random vs. K-Means++ initialization, local optima

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