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Atlassian·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Atlassian data scientist interview with a pretty gnarly algorithmic problem that felt more like a competitive programming round than anything I expected for this role. The problem was well-defined but the solution space was wider than it looked at first glance.

Questions Asked (1)

Q1

Given an array of n integers and an integer k, place k cluster centers to minimize the maximum L1 distance from any point to its nearest center. Return that minimum possible distance.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I sorted the array first which was the right instinct, but then I fumbled around trying to brute force placements before realizing binary search on the answer was the cleaner path.

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AI HintsAI Generated

Suggested Approach

Recognize this as a minimax facility location problem on a line (1D) with L1 distance, which reduces to placing k centers to minimize the maximum gap between consecutive centers. Use binary search on the answer D, and for each D, greedily check if k centers can cover all points such that every point is within D of some center. The minimal feasible D is the answer.

Pro tip: Clarify that L1 distance in 1D is just absolute difference, so the problem simplifies to covering points with intervals of length 2D; this shows you can reduce complexity and avoid overcomplicating with general clustering algorithms.

1. Clarify and Restate

Confirm that the array is 1D and distance is L1 (absolute difference). Restate the goal: minimize the maximum distance from any point to its nearest of k centers.

2. Binary Search on Answer

Observe that if a distance D is feasible, any larger D is also feasible. Binary search the minimal D over the range [0, max-min].

3. Feasibility Check

For a given D, sort the points and greedily place a center at the leftmost uncovered point + D, covering all points within D. Count centers needed; feasible if ≤ k.

4. Complexity and Edge Cases

Analyze time: O(n log n + n log(max-min)) due to sorting and binary search. Handle edge cases: k ≥ n (answer 0), k=1 (answer (max-min)/2), duplicate points.

5. Discuss Trade-offs

Mention alternative approaches (e.g., DP for exact k centers) and why greedy+binary search is optimal and simpler for this minimax objective.

Key Points to Mention

  • Reduction to 1D k-center problem with L1 distance
  • Binary search on the answer (minimax)
  • Greedy interval covering for feasibility check
  • Time complexity: O(n log n + n log R) where R is range
  • Handling of edge cases (k ≥ n, k=1, duplicates)
  • Why this approach is optimal and practical for large n

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