Upstart·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Second technical round at Upstart for a Data Scientist role, focused entirely on a sliding window coding problem. Pretty standard algorithmic interview but the K distinct characters twist required more careful implementation than I expected.

Questions Asked (1)

Given a string and an integer K, write an algorithm that finds the length of the longest substring containing at most K distinct characters.

Algorithms & Data Structures

Author's notes

I knew it was a sliding window problem pretty fast, which felt good, but then I fumbled the shrink condition for longer than I'd like to admit.

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AI HintsAI Generated

Suggested Approach

Use the sliding window technique with two pointers to maintain a window that contains at most K distinct characters. Expand the right pointer to include new characters, and when the distinct count exceeds K, shrink the window from the left until it's valid again. Track the maximum window length throughout.

Pro tip: Clarify edge cases upfront (e.g., K=0, empty string) and mention that the algorithm runs in O(n) time with O(K) space, which is optimal. Also, briefly discuss how you would test the solution with examples.

1. Clarify requirements and edge cases

Confirm the definition of 'substring' (contiguous) and 'distinct characters'. Ask about edge cases: empty string, K=0, K >= number of distinct characters in the string.

2. Choose the sliding window approach

Explain that a brute-force check of all substrings would be O(n^2) or worse, so a sliding window with two pointers is optimal. Maintain a frequency map of characters in the current window.

3. Define window expansion and contraction rules

Expand the right pointer to include a new character. If the number of distinct characters exceeds K, move the left pointer forward, updating the frequency map, until the window is valid again.

4. Track the maximum length

After each expansion (and contraction if needed), update the maximum length if the current window size is larger. Continue until the right pointer reaches the end of the string.

5. Analyze complexity and test

State that the time complexity is O(n) because each character is processed at most twice, and space is O(K) for the frequency map. Walk through a small example to verify correctness.

Key Points to Mention

Sliding window technique with two pointers (left and right).
Use a hash map (dictionary) to count character frequencies in the current window.
Condition to shrink: when the number of distinct characters (size of hash map) exceeds K.
Update the maximum length after each valid window.
Time complexity O(n) and space complexity O(K).
Handle edge cases: empty string, K=0, K >= distinct characters.

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