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Microsoft·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Microsoft Data Scientist round that was basically one meaty coding/design question about org chart traversal. Not a bad experience but the edge case discussion at the end was where things got real.

Questions Asked (1)

Q1

You're given a flat list of employee-manager pairs representing a company hierarchy. Build a tree from it, then return all employees level by level from the top down. Also discuss your data structures, complexity, and how you'd handle bad input like cycles, duplicate roots, or missing managers.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The tree-building part was fine, I used a hashmap to store children by parent id and found the root by looking for whoever had a null manager.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input format and expected output, then outline a BFS-based tree construction using a hash map for adjacency and a queue for level-order traversal. Discuss validation for cycles, duplicate roots, and missing managers, and analyze time and space complexity.

Pro tip: Proactively mention that you would validate the input by checking for cycles and ensuring exactly one root, and suggest using topological sorting or union-find for cycle detection. This shows you think about robustness beyond the happy path.

1. Clarify Requirements and Edge Cases

Ask clarifying questions about input format, expected output, and how to handle invalid data. Confirm whether the hierarchy is guaranteed to be a tree or if it may contain cycles, multiple roots, or missing managers.

2. Design Data Structures

Use a hash map to store each employee's direct reports (adjacency list) and a hash set to track all employees. Identify the root by finding the employee who never appears as a child, or by using a parent map.

3. Build the Tree and Validate

Iterate through the pairs to populate the adjacency list and parent map. Detect cycles using DFS or union-find, and ensure there is exactly one root. Handle missing managers by either treating them as roots or raising an error.

4. Perform Level-Order Traversal

Use a queue to perform BFS starting from the root, processing nodes level by level. Collect employees at each level into a list of lists or a flat list with level markers.

5. Analyze Complexity and Discuss Trade-offs

State that time complexity is O(N) for N employees, and space complexity is O(N) for the map and queue. Discuss alternative approaches like DFS with level tracking and their trade-offs.

Key Points to Mention

  • Use a hash map (dictionary) to represent the tree as an adjacency list for O(1) average-time lookups.
  • Identify the root by finding the employee not present as a child in any pair; if multiple or none, handle as invalid input.
  • Detect cycles using DFS with a recursion stack or union-find; if a cycle exists, the input is not a valid tree.
  • Handle missing managers by either treating them as roots (if they have no parent) or by raising an error if the hierarchy is expected to be complete.
  • Perform BFS with a queue to return employees level by level; optionally track levels using a delimiter or by processing level sizes.
  • Time complexity is O(N) and space complexity is O(N), where N is the number of employees; mention that sorting or other operations could increase complexity.

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