Tencent·Data Scientist·Technical Phone Screen·Senior

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Jun 2026

Summary

Technical screen for a Data Scientist role at Tencent, basically one long coding question about implementing k-means from scratch with a bunch of requirements stacked on top of each other. Pretty intense for a phone screen.

Questions Asked (1)

Implement k-means clustering from scratch in Python (no scikit-learn) with k-means++ initialization, vectorized NumPy distance computation, early stopping on centroid shift, empty cluster handling, optional sample weights, deterministic random state, complexity analysis, and a testing/debugging strategy.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This question is basically eight questions dressed up as one.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and outlining the algorithm's structure, then implement each component (k-means++ initialization, vectorized assignment, centroid update with empty cluster handling and sample weights) while explaining design choices. Conclude with complexity analysis and a testing strategy that covers edge cases and correctness.

Pro tip: Mention that you'll use a deterministic random state and write unit tests for each component (e.g., k-means++ probabilities, empty cluster handling) to ensure reproducibility and robustness, which shows production-level thinking.

1. Clarify Requirements and Outline

Confirm input/output expectations, constraints (e.g., sample weights, deterministic seed), and sketch the overall algorithm flow before coding.

2. Implement Core Components

Code k-means++ initialization, vectorized distance computation using NumPy broadcasting, and centroid update with sample weights and empty cluster handling.

3. Add Convergence and Early Stopping

Implement early stopping based on centroid shift (e.g., Frobenius norm) and set a maximum number of iterations to avoid infinite loops.

4. Analyze Complexity and Trade-offs

Discuss time and space complexity, and trade-offs between vectorization, memory usage, and scalability.

5. Design Testing and Debugging Strategy

Outline unit tests for each component, edge cases (empty clusters, single cluster, weighted samples), and methods to verify correctness (e.g., compare with sklearn on small data).

Key Points to Mention

k-means++ initialization: probabilistic selection of initial centroids to improve convergence.
Vectorized distance computation: use NumPy broadcasting to compute pairwise distances efficiently.
Empty cluster handling: reassign empty centroids to the farthest point from its centroid or reinitialize randomly.
Sample weights: incorporate weights in centroid updates by computing weighted means.
Deterministic random state: set a seed for reproducibility in initialization and any random operations.
Complexity analysis: O(n * k * d * i) time per iteration, where n is samples, k clusters, d dimensions, i iterations; space O(n * d + k * d).
Testing strategy: unit tests for initialization, assignment, update; integration tests on synthetic data; compare with scikit-learn.

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