TikTok·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Python tech screen for a Data Scientist role at TikTok. Pretty focused, just one coding problem the whole time, which I wasn't expecting to be as tricky as it turned out to be.

Questions Asked (1)

Given a dictionary where each key maps to a list of numbers, write a Python function that returns a new dictionary where each value is the mean of the unique numbers from the original list.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I jumped straight to set() for deduplication and it worked fine, but I second-guessed myself mid-explanation and started rambling about order preservation, which was completely irrelevant for computing a mean.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then outline a solution using a dictionary comprehension with set() to deduplicate and statistics.mean or sum/len to compute the mean. Write clean, efficient code and discuss trade-offs like handling empty lists and floating-point precision.

Pro tip: Mention that using set() removes duplicates in O(n) time and that you'd handle empty lists gracefully to avoid division by zero, showing attention to edge cases and production readiness.

1. Clarify requirements and edge cases

Ask about input types, empty lists, non-numeric values, and expected output format. Confirm that 'unique numbers' means deduplication before averaging.

2. Outline the algorithm

For each key-value pair, convert the list to a set to get unique numbers, then compute the mean by summing and dividing by the count. Use a dictionary comprehension for conciseness.

3. Write the code

Implement the function, handling empty lists by returning 0 or None as appropriate. Use statistics.mean or manual sum/len for clarity.

4. Test with examples

Walk through a sample input, including edge cases like empty lists and duplicates, to verify correctness and discuss time/space complexity.

5. Discuss trade-offs and optimizations

Talk about using set() for O(n) deduplication, potential floating-point issues, and whether to use statistics.mean for readability or manual calculation for performance.

Key Points to Mention

Use set() to efficiently deduplicate numbers before computing the mean.
Handle empty lists to avoid division by zero, returning 0 or None based on context.
Leverage dictionary comprehension for a clean, Pythonic solution.
Consider using statistics.mean for readability or sum()/len() for performance.
Discuss time complexity: O(n) for deduplication and O(m) for mean calculation per list.
Mention potential floating-point precision issues and how to round if needed.

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