Robinhood·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Robinhood data scientist interview with a coding problem built around streaming tick data and OHLC aggregation. The question was more engineering-flavored than I expected for a DS role, which threw me off a bit.

Questions Asked (1)

Write a Python function that parses a string of price:timestamp pairs, buckets them into 10-second intervals, computes OHLC (open, high, low, close) for each bucket, and forward-fills any empty intervals using the last known price.

Algorithms & Data StructuresSystem DesignAPI & Integrations

Author's notes

I didn't expect a pure coding question this close to systems/data engineering territory for a DS interview.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the input format and edge cases, then outline a solution that parses the string, buckets by 10-second intervals, computes OHLC, and forward-fills missing intervals. Focus on clean code, efficient data structures, and handling of unsorted or duplicate timestamps.

Pro tip: Mention that forward-filling is common in financial time series to handle missing data, and that using a sorted list of intervals ensures correctness. Also, discuss how you would test the function with edge cases like empty input or single data point.

1. Clarify requirements and edge cases

Ask about input format (e.g., 'price:timestamp' separated by commas or newlines), timestamp units (seconds since epoch?), and handling of unsorted data, duplicates, or missing intervals. Confirm output format (e.g., list of OHLC tuples).

2. Parse and bucket data

Split the string into pairs, parse each into (price, timestamp), and compute the bucket index as timestamp // 10. Group prices by bucket, preserving order for open/close.

3. Compute OHLC per bucket

For each bucket, compute open (first price), high (max), low (min), close (last price). Store results in a dictionary keyed by bucket index.

4. Forward-fill empty intervals

Determine the range of bucket indices from min to max. For any missing bucket, use the previous bucket's close as open, high, low, and close (or just carry forward the last known price).

5. Return sorted results

Convert the dictionary to a sorted list of OHLC values by bucket index and return. Discuss time/space complexity and potential optimizations.

Key Points to Mention

Handling unsorted timestamps by sorting or using a dictionary to group by bucket.
Efficient computation of OHLC using min, max, and first/last elements.
Forward-filling logic: using the previous close as the OHLC for empty intervals.
Edge cases: empty input, single data point, duplicate timestamps, and large gaps.
Time and space complexity: O(n log n) if sorting, O(n) with dictionary.
Testing strategy: unit tests for normal cases, edge cases, and performance.

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