Google·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Interviewed for a Data Scientist role at Google and got a classic anagram problem dressed up as a backend feature requirement. Nothing too wild, but the complexity follow-up kept it from being totally routine.

Questions Asked (1)

Write a Python function that checks whether two input strings are anagrams of each other, and explain the time and space complexity of your solution.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with sorted strings first because it's the easiest to explain out loud.

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AI HintsAI Generated

Suggested Approach

Start by clarifying assumptions (e.g., case sensitivity, whitespace, character set) and then present a solution using a frequency count (e.g., dictionary or Counter). Compare counts to determine if the strings are anagrams, and then analyze time and space complexity, discussing trade-offs with sorting-based approaches.

Pro tip: Mention that for large inputs, a counting approach is O(n) time and O(1) space if the character set is fixed (e.g., ASCII), but O(k) space for Unicode. Also, note that sorting is O(n log n) and may be simpler but less efficient for large strings.

1. Clarify requirements

Ask about case sensitivity, whitespace handling, and character set (e.g., ASCII vs Unicode). This shows attention to detail and avoids incorrect assumptions.

2. Choose an approach

Decide between frequency counting (O(n) time) and sorting (O(n log n) time). Explain why counting is generally more efficient for large inputs.

3. Implement the function

Write clean Python code using collections.Counter or a manual dictionary to count characters. Include an early length check for efficiency.

4. Analyze complexity

State time complexity: O(n) for counting, O(n log n) for sorting. Space complexity: O(k) where k is the number of unique characters, or O(1) if character set is fixed.

5. Discuss trade-offs

Compare counting vs sorting in terms of readability, performance, and memory. Mention edge cases like empty strings or different lengths.

Key Points to Mention

Anagrams must have the same length and same character frequencies.
Using a hash map (dictionary) to count characters gives O(n) time and O(k) space.
Sorting both strings and comparing gives O(n log n) time and O(n) space (due to sorting).
For fixed character sets (e.g., ASCII), space can be considered O(1).
Early exit if lengths differ saves time.
Python's collections.Counter provides a concise implementation.

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