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Netflix·Data Scientist·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Netflix data scientist interview with a surprisingly algorithmic coding question. The problem was more CS-heavy than I expected for a DS role, felt like I was interviewing for a software engineer position halfway through.

Questions Asked (1)

Q1

Given an array of integers, find the length of the longest strictly increasing contiguous subarray you can get by removing at most one element from it. Return the length and the 0-based index pair [l, r] of that subarray in the original array. If there are ties, return the lexicographically smallest pair. Must run in O(n) time with O(1) extra space.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This wrecked me a little.

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AI HintsAI Generated

Suggested Approach

Use a sliding window with two pointers to maintain a window that can be made strictly increasing by removing at most one element. Track the longest valid window and its indices, updating when a longer window is found or when the same length yields a lexicographically smaller start index. Ensure O(n) time by moving pointers only forward and O(1) space by using a few variables.

Pro tip: Clarify that 'removing at most one element' means the resulting subarray must be strictly increasing after removal, and that the indices refer to the original array. Emphasize that the lexicographically smallest pair is determined by comparing the start index first, then the end index.

1. Understand the problem and constraints

Restate the problem: find the longest contiguous subarray that becomes strictly increasing after removing at most one element, and return its length and [l, r] in the original array. Note the O(n) time and O(1) space constraints.

2. Design a sliding window approach

Use two pointers (left and right) to represent the current window. Maintain a count of 'bad' adjacent pairs (where arr[i] >= arr[i+1]) within the window. Expand right and shrink left as needed to keep the count ≤ 1.

3. Track the best window

When the window is valid (bad count ≤ 1), compute its length. If it's longer than the current best, update best length and indices. If equal, compare start indices and update if the new start is smaller.

4. Handle edge cases and tie-breaking

Consider arrays of length 0 or 1, and ensure that when multiple windows have the same length, the one with the smallest starting index is chosen. Also, verify that removing one element indeed makes the window strictly increasing.

5. Analyze complexity and trade-offs

Explain that each element is visited at most twice (by left and right pointers), giving O(n) time. Space is O(1) as only a few variables are used. Discuss potential pitfalls like off-by-one errors in index tracking.

Key Points to Mention

  • Sliding window technique with two pointers to maintain a window with at most one 'bad' adjacent pair.
  • Definition of a 'bad' pair: arr[i] >= arr[i+1], which violates strict increasing order.
  • Tracking the longest window and updating for lexicographically smallest [l, r] when lengths tie.
  • Handling edge cases: empty array, single element, and arrays already strictly increasing.
  • Time complexity O(n) because each element is processed at most twice; space complexity O(1).
  • Clarifying that the removal is optional and the subarray must be contiguous in the original array.

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