LinkedIn·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

LinkedIn data scientist interview with a bot detection problem that required both SQL and Python. Pretty applied, which I liked, but the open-ended definition of 'bot' left me second-guessing myself the whole time.

Questions Asked (1)

Given a table of page view events with member IDs and timestamps, write SQL and Python to identify and remove or flag bot users. You get to define what counts as a bot, so explain your reasoning.

Product Analytics & MetricsAlgorithms & Data StructuresData Modeling

Author's notes

The open definition is what tripped me up at first.

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AI HintsAI Generated

Suggested Approach

Start by defining bot behavior based on domain knowledge, such as abnormal event frequency, repetitive patterns, or unrealistic session durations. Then, write SQL queries to flag suspicious users using window functions and aggregations, and Python code to apply more sophisticated heuristics or machine learning. Finally, explain how you would validate and iterate on the bot detection logic.

Pro tip: Demonstrate awareness of trade-offs: false positives (flagging real users) vs. false negatives (missing bots), and propose a feedback loop to refine thresholds. Also, mention that bot detection is an ongoing process, not a one-time task.

1. Define bot criteria

Based on the data schema and business context, specify quantitative and qualitative rules that characterize bot behavior, such as >100 page views per minute, identical timestamps, or uniform inter-event times.

2. SQL detection and flagging

Write SQL queries using window functions (e.g., LAG, LEAD) and aggregations (e.g., COUNT, AVG) to compute per-user metrics and flag users exceeding thresholds. Use CTEs for readability.

3. Python implementation

Load the data into a pandas DataFrame, compute similar features, and apply the same or more advanced rules (e.g., isolation forest, clustering) to flag bots. Show code for flagging and optionally removing.

4. Validation and iteration

Discuss how to validate the approach using labeled data or manual inspection, and how to iterate on thresholds to balance precision and recall.

5. Handling flagged users

Explain what to do with flagged users: remove from analysis, exclude from metrics, or flag for further review, considering business impact.

Key Points to Mention

Use of window functions (e.g., LAG, LEAD) to compute time differences between events per user.
Aggregations to compute event counts per user per time window (e.g., events per minute).
Statistical thresholds or anomaly detection techniques (e.g., z-score, IQR, isolation forest).
Trade-offs between false positives and false negatives, and how to set thresholds.
Scalability considerations for large datasets (e.g., using SQL efficiently, sampling in Python).
Business context: why bot detection matters for metrics like DAU/MAU and engagement.

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