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This question is commonly asked for Data Scientist candidates at Uber during technical interviews.

Implement Streaming Clustering for Numbers

Last updated: May 3, 2026

Quick Overview

This question evaluates a data scientist's competency in online clustering for numeric streams, testing understanding of streaming algorithms, bounded-memory cluster summaries, online updates, and issues including initialization, convergence behavior, outlier handling, and concept drift.

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Implement Streaming Clustering for Numbers

Uber

Apr 10, 2026, 12:00 AM

mediumData ScientistOnsiteMachine Learning

You receive a continuous stream of numeric values. Choose an appropriate clustering algorithm and implement it so that each incoming number can be assigned to a cluster while using bounded memory.

Clarify and address the following:

The stream is potentially unbounded, so storing all historical values is not allowed.
You may assume the number of clusters k is given, or explain how you would choose it.
The algorithm should update cluster summaries online as new values arrive.
The implementation should expose at least two operations: add(value) to process a new number and get_clusters() to return the current cluster centers or summaries.
Discuss initialization, convergence behavior, outliers, concept drift, and how you would test correctness.

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