Design a Maps Address Search Bar

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Quick Overview

This question evaluates proficiency in data science and product design for search systems, including query interpretation, candidate generation, relevance and distance-based ranking, typo tolerance, geospatial reasoning, metrics, experimentation, and bias and failure-mode analysis.

Design a Maps Address Search Bar

Company: Uber

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

Design the search experience for a map application's address bar, similar to the search box in Google Maps. The system should handle multiple user intents and input types, including: - Exact street addresses. - Place or business names. - Fuzzy matches and partial queries. - Typographical errors. - Location-aware results based on the user's current location or map viewport. - Ranking by relevance, distance, and popularity. Describe the end-to-end product and data science design. Include candidate generation, ranking, metrics, experimentation, bias and failure modes, and how you would evaluate whether the search bar improves the user experience.

Overview: This question evaluates proficiency in data science and product design for search systems, including query interpretation, candidate generation, relevance and distance-based ranking, typo tolerance, geospatial reasoning, metrics, experimentation, and bias and failure-mode analysis.

Read the full Uber Data Scientist interview experience this question came from

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Uber
Apr 10, 2026
mediumData ScientistOnsiteAnalytics & Experimentation
5
0

Design the search experience for a map application's address bar, similar to the search box in Google Maps.

The system should handle multiple user intents and input types, including:

  • Exact street addresses.
  • Place or business names.
  • Fuzzy matches and partial queries.
  • Typographical errors.
  • Location-aware results based on the user's current location or map viewport.
  • Ranking by relevance, distance, and popularity.

Describe the end-to-end product and data science design. Include candidate generation, ranking, metrics, experimentation, bias and failure modes, and how you would evaluate whether the search bar improves the user experience.

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