Two Sigma·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Two Sigma data scientist interview with a coding problem that looked like a simple merge exercise but had enough edge cases to slow me down. The problem was algorithmic but clearly motivated by real data pipeline work, which I appreciated.

Questions Asked (1)

Given two time-sorted lists of sensor readings (temperature and humidity), each record containing a timestamp, city, and value, for every temperature record find the most recent humidity reading from the same city where the humidity timestamp is less than or equal to the temperature timestamp. Return null if no such humidity record exists.

Algorithms & Data StructuresData Modeling

Author's notes

My first instinct was to just do a nested loop and move on, but I caught myself and realized that's embarrassing given both lists are already sorted.

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AI HintsAI Generated

Suggested Approach

Use a two-pointer or hash map approach to efficiently merge the two sorted lists, ensuring O(n + m) time complexity. For each temperature record, maintain the latest humidity reading per city that satisfies the timestamp condition, and handle missing cities by returning null.

Pro tip: Emphasize the importance of handling edge cases like duplicate timestamps, empty lists, and cities with no humidity data, and discuss how the solution scales with large datasets.

1. Clarify requirements and assumptions

Confirm the data format, sorting order, and whether timestamps are unique. Ask about expected output format and any constraints on memory or time.

2. Choose an efficient algorithm

Select a two-pointer technique or a hash map to track the latest humidity per city. Explain why this achieves optimal time complexity.

3. Walk through the algorithm

Describe step-by-step how to iterate through both lists, update the latest humidity for each city, and assign the correct humidity to each temperature record.

4. Handle edge cases

Discuss scenarios like empty lists, no matching humidity, duplicate timestamps, and cities present in only one list. Explain how the algorithm handles them.

5. Analyze complexity and test

State the time and space complexity, and suggest test cases to validate correctness, including large datasets and boundary conditions.

Key Points to Mention

Two-pointer technique for merging sorted lists
Hash map to store the latest humidity per city
Time complexity O(n + m) and space complexity O(k) where k is number of cities
Handling null when no humidity record exists
Edge cases: empty lists, duplicate timestamps, cities with no humidity
Scalability for large datasets and potential for streaming data

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