Uber·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Uber data scientist interview with a meaty streaming ML question that had a lot of moving parts. The kind of problem where you can tell they want to see how you think through constraints, not just whether you know k-means.

Questions Asked (1)

You're receiving a continuous, potentially unbounded stream of numeric values. Design and implement an online clustering solution that assigns each incoming number to a cluster using bounded memory. Your solution should support an add(value) operation and a get_clusters() operation, and you should discuss initialization, convergence, outliers, concept drift, and how you'd test it.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one took me a minute to even figure out where to start.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., expected stream rate, memory limits, cluster shape assumptions) and then propose an online clustering algorithm like online k-means or a streaming variant such as StreamKM++ or BIRCH. Structure your answer around the required operations (add and get_clusters), then discuss initialization, convergence, outliers, concept drift, and testing, emphasizing bounded memory and practical trade-offs.

Pro tip: Demonstrate awareness of production concerns by mentioning that you would monitor cluster stability and drift metrics in real-time, and have a fallback to re-initialize clusters if drift is detected, ensuring robustness for Uber-scale data.

1. Clarify requirements and constraints

Ask about stream characteristics (rate, value range, dimensionality), memory bounds, latency requirements, and whether clusters are expected to be spherical or arbitrary. This ensures your design aligns with the interviewer's expectations.

2. Choose an online clustering algorithm

Select a bounded-memory algorithm such as online k-means with reservoir sampling, StreamKM++, or BIRCH. Explain how it maintains cluster summaries (e.g., centroids, counts) and updates them incrementally with each new point.

3. Design add(value) and get_clusters() operations

Describe how add(value) assigns the point to the nearest cluster (or creates a new one) and updates the cluster summary. get_clusters() returns the current cluster representatives (e.g., centroids) and possibly cluster sizes.

4. Address initialization, convergence, outliers, and concept drift

Discuss strategies for initializing clusters (e.g., first k points, k-means++ on a sample), ensuring convergence (e.g., learning rate decay), handling outliers (e.g., distance threshold, separate outlier cluster), and adapting to drift (e.g., sliding window, fading factors, periodic re-clustering).

5. Outline testing and evaluation

Propose testing with synthetic streams (e.g., Gaussian mixtures with drift), measuring cluster quality (e.g., silhouette score on held-out data), memory usage, and update latency. Also mention A/B testing in production if applicable.

Key Points to Mention

Bounded memory: use fixed-size summaries (centroids, counts) and avoid storing all points; consider reservoir sampling for initialization.
Online k-means with learning rate: update centroids incrementally as new points arrive, with a decaying learning rate for convergence.
Outlier handling: use a distance threshold to identify outliers and either assign to a separate cluster or ignore them to avoid skewing centroids.
Concept drift: employ fading factors, sliding windows, or periodic re-clustering to adapt to changing data distributions.
Initialization: use a small sample to seed clusters (e.g., k-means++ on first N points) to avoid poor initial centroids.
Evaluation: test with synthetic and real streams, measure cluster quality, memory footprint, and update throughput; monitor drift in production.

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