Two Sigma·Data Scientist·Online Assessment (OA)·Intermediate

Intermediate

May 2026

Summary

Two Sigma data scientist interview with a coding problem that looked like a clean little data pipeline task but had enough edge cases to keep you honest. Single question, no behavioral stuff from what I can tell.

Questions Asked (1)

Given a list of sensor readings as [timestamp, value] pairs where values can be null and timestamps can repeat, clean the series by merging duplicates (averaging known values at the same timestamp), sorting, interpolating interior missing values linearly, and forward/backward filling boundary gaps. Return one [timestamp, value] pair per distinct timestamp with all values filled.

Algorithms & Data StructuresData Modeling

Author's notes

The merge step is where I would've tripped up first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and edge cases, then outline a step-by-step algorithm: group by timestamp, average non-null values, sort timestamps, interpolate interior nulls linearly, and fill boundary nulls via forward/backward fill. Emphasize time and space complexity and discuss potential optimizations or trade-offs.

Pro tip: Demonstrate awareness of real-world data issues by mentioning how you'd handle duplicate timestamps with all null values and ensuring the interpolation is robust to floating-point precision. Also, proactively discuss testing with edge cases like empty input or single timestamp.

1. Clarify requirements and edge cases

Ask about input size, expected output format, and how to handle cases like all-null values at a timestamp or empty input. Confirm that timestamps are numeric and sortable.

2. Merge duplicates and average known values

Group readings by timestamp, compute the average of non-null values for each timestamp, and represent timestamps with all nulls as null. Use a dictionary or hash map for O(n) grouping.

3. Sort timestamps and prepare for interpolation

Sort the distinct timestamps in ascending order. Convert the grouped data into a sorted list of (timestamp, value) pairs, preserving nulls for missing values.

4. Interpolate interior missing values linearly

Iterate through the sorted list; for each null value that has non-null neighbors on both sides, compute the linearly interpolated value based on timestamp distances. Handle consecutive nulls by interpolating between the nearest known values.

5. Fill boundary gaps and return result

For leading nulls, forward fill from the first known value; for trailing nulls, backward fill from the last known value. If all values are null, decide on a default (e.g., return nulls or raise an error). Return the cleaned list.

Key Points to Mention

Time and space complexity: O(n log n) due to sorting, O(n) for grouping and interpolation.
Handling duplicate timestamps: averaging only non-null values; if all null, keep as null.
Linear interpolation formula: value = v1 + (v2 - v1) * (t - t1) / (t2 - t1).
Edge cases: empty input, single timestamp, all null values, consecutive nulls, and boundary gaps.
Use of appropriate data structures: hash map for grouping, sorted list for interpolation.
Potential optimizations: using pandas or numpy for vectorized operations if allowed, or streaming algorithms for large datasets.

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