PayPal·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2024Remote

Summary

PayPal data scientist interview with a coding-heavy technical screen focused on pandas and feature engineering for transaction data. Two related questions, both practical and grounded in real pipeline work.

Questions Asked (2)

Write a Python function that filters out users who have fewer than 100 transactions in a given calendar month.

Product Analytics & MetricsAlgorithms & Data Structures

Author's notes

I went straight to groupby and size() which was the right instinct, but I initially forgot to group by both user and month together.

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AI HintsAI Generated

Suggested Approach

Clarify the input data structure and definition of 'calendar month', then design a function that groups transactions by user and month, counts them, and filters users with counts below 100. Write clean, efficient Python code using appropriate data structures and discuss edge cases.

Pro tip: Demonstrate production awareness by discussing how to handle large datasets (e.g., using generators or chunking) and how to make the function reusable for different thresholds and date ranges.

1. Clarify requirements and assumptions

Ask about the input format (e.g., list of transactions, DataFrame), the definition of 'calendar month' (e.g., year-month), and whether the function should return user IDs or full user records.

2. Design the algorithm

Outline a plan: iterate through transactions, filter by the given month, count transactions per user, then filter users with count >= 100. Consider using a dictionary for counting.

3. Implement the function

Write Python code that follows the design, using clear variable names and efficient operations. Handle date parsing if needed.

4. Test with edge cases

Mention testing with empty input, users with exactly 100 transactions, transactions outside the month, and invalid dates.

5. Discuss scalability and extensions

Talk about how the solution could be adapted for large datasets (e.g., using pandas or SQL) and how to parameterize the threshold and month.

Key Points to Mention

Data structure choice: dictionary for counting, or pandas groupby for vectorized operations
Date handling: parsing dates and filtering by year and month
Efficiency: time complexity O(n) and space complexity O(u) where u is number of users
Edge cases: empty input, users with exactly 100 transactions, transactions spanning multiple months
Scalability: using chunking or database queries for large datasets
Code clarity: meaningful variable names, docstrings, and type hints

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

Write a Python function that computes each user's average time between consecutive transactions, in seconds.

Product Analytics & MetricsData Modeling

Author's notes

This one I liked more.

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AI HintsAI Generated

Suggested Approach

Clarify the input data structure (e.g., a DataFrame with user_id and transaction timestamp columns) and the expected output format. Then, sort transactions per user by time, compute the differences between consecutive timestamps, and take the average per user. Implement efficiently using pandas groupby and diff operations.

Pro tip: Mention edge cases like users with only one transaction (average undefined) and how to handle them (e.g., return NaN or 0). Also, note that timestamps should be in a consistent format and consider timezone awareness.

1. Clarify requirements and data

Ask about the input data format (e.g., pandas DataFrame, list of dicts) and the expected output (e.g., dictionary mapping user_id to average seconds). Confirm if timestamps are strings or datetime objects.

2. Sort and group by user

Sort the data by user_id and timestamp to ensure chronological order within each user. Group by user_id to process each user's transactions separately.

3. Compute time differences

For each user, calculate the time difference between consecutive transactions in seconds. Use pandas diff() on the timestamp column after sorting.

4. Calculate average per user

For each user, compute the mean of the time differences. Handle users with fewer than two transactions appropriately (e.g., return NaN or 0).

5. Return results in desired format

Return a dictionary or DataFrame with user_id and average time between transactions. Ensure the output is clear and matches the expected format.

Key Points to Mention

Data sorting: ensure transactions are sorted chronologically per user before computing differences.
Time unit conversion: convert time differences to seconds (e.g., using .dt.total_seconds() if using pandas Timedelta).
Handling users with a single transaction: decide whether to exclude them or return NaN/0.
Efficiency: use vectorized operations (pandas groupby and diff) rather than loops for large datasets.
Edge cases: missing values, duplicate timestamps, and timezone consistency.
Output format: clarify whether to return a dictionary, DataFrame, or series.

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