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Robinhood·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Robinhood Analytics Engineer technical screen, one meaty pandas question that took up basically the whole slot. The session bucketing logic sounds deceptively simple until you actually start coding it.

Questions Asked (1)

Q1

Given a CSV of user activity logs with columns including user_id, event_time, and topic, write pandas code to group each user's events into sessions where consecutive events are no more than 30 minutes apart. For each session, compute session_start, session_end (capped at last event time plus 30 minutes), the number of distinct topics, and the event count. Return a DataFrame sorted by user_id and session_start. Also discuss the trade-offs between a vectorized approach and groupby-apply.

Product Analytics & MetricsTechnical Trade-offsAlgorithms & Data Structures
Author's notes

I started with groupby-apply because it felt natural, sort by event_time within each user group, compute time diffs, label session breaks with cumsum.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the sessionization logic and edge cases, then outline a vectorized pandas solution using sort, diff, and cumsum to assign session IDs, followed by groupby aggregations. Finally, discuss the trade-offs between vectorized and groupby-apply approaches in terms of performance, readability, and scalability.

Pro tip: Mention that you would validate the sessionization with a small sample and consider using numpy for faster diff calculations, showing attention to both correctness and performance.

1. Clarify requirements and edge cases

Confirm the definition of a session (consecutive events ≤30 minutes apart), how to handle ties, and whether sessions can span days. Discuss the cap on session_end.

2. Outline vectorized approach

Describe sorting by user_id and event_time, computing time differences, flagging new sessions when diff > 30 minutes, and using cumsum to assign session IDs.

3. Compute session metrics

Explain grouping by user_id and session_id to calculate session_start (min event_time), session_end (min of max event_time + 30 min and last event_time + 30 min), distinct topics (nunique), and event count (size).

4. Sort and return final DataFrame

Sort the resulting DataFrame by user_id and session_start, and ensure the output columns match the requirements.

5. Discuss trade-offs

Compare vectorized vs groupby-apply: vectorized is faster and more memory-efficient for large data, while groupby-apply is more readable and flexible for complex logic but can be slower.

Key Points to Mention

  • Sessionization logic: consecutive events within 30 minutes belong to the same session.
  • Vectorized operations: sort_values, diff, cumsum for session ID assignment.
  • Groupby aggregations: min, max, nunique, size for session metrics.
  • Trade-offs: performance vs readability, scalability, and maintainability.
  • Edge cases: single-event sessions, events exactly 30 minutes apart, and session_end capping.
  • Use of numpy for faster time difference calculations.

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