DoorDash·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

DoorDash technical phone screen for a Data Scientist role, heavy on algorithmic coding. One meaty sliding window problem that took up most of the session, with a pretty specific correctness bar around edge cases and complexity.

Questions Asked (1)

Implement a function that returns the start and end indices of the shortest substring of s containing all characters in t with at least their required frequencies. Must run in O(n) time using a sliding window approach, and handle inputs up to 2 million characters.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is the classic minimum window substring but with a stricter harness than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then explain the sliding window technique with two pointers and a frequency map. Emphasize how the window expands and contracts to find the shortest valid substring, and analyze time and space complexity to show O(n) time and O(k) space.

Pro tip: Mention that you would use an array of size 128 or 256 for character counts instead of a hash map to achieve constant-time operations and better performance for large inputs. Also, discuss how you would handle the 2 million character constraint by avoiding unnecessary string copies and using efficient data structures.

1. Clarify requirements and edge cases

Ask about character set (ASCII vs Unicode), case sensitivity, and what to return if no valid substring exists. Confirm that t's characters must appear at least as many times as in t.

2. Explain the sliding window approach

Describe using two pointers (left and right) to maintain a window. Expand right to include characters until the window is valid, then contract left to minimize the window while keeping it valid.

3. Detail the frequency tracking and validation

Use a frequency array for t's characters and a counter for how many required characters are satisfied. Update counts as the window expands and contracts, and track the minimum window length and start index.

4. Analyze complexity and optimize for large inputs

State that each character is visited at most twice, giving O(n) time. Use O(1) space for fixed alphabet. For 2 million characters, emphasize avoiding substring creation and using efficient loops.

5. Test with examples and edge cases

Walk through a small example to demonstrate correctness. Mention testing with empty strings, no valid substring, and large inputs to ensure performance.

Key Points to Mention

Sliding window with two pointers for O(n) time complexity
Frequency array (or hash map) to track required character counts
A counter to track how many unique characters have met their required frequency
Expanding and contracting the window while updating the minimum length
Handling edge cases: empty strings, no valid substring, and large input size
Space complexity: O(1) for fixed alphabet or O(k) for k unique characters

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