Snapchat·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Snapchat Data Scientist interview with a pandas wrangling task on story engagement data. Pretty straightforward if you know your groupby patterns, but the filtering step tripped me up a bit.

Questions Asked (1)

Given a story engagement dataset, use pandas to find users who have posted at least three group stories, and separately aggregate total views and likes broken down by story type.

Product Analytics & MetricsData Modeling

Author's notes

I started with the aggregation part first because it felt easier, groupby story_type and sum views and likes, that was fine.

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AI HintsAI Generated

Suggested Approach

First, clarify the dataset schema and definitions (e.g., what constitutes a 'group story', how views/likes are recorded). Then, use pandas groupby and filtering to identify users with at least three group stories, and separately aggregate views and likes by story type. Finally, validate results and discuss any assumptions or data quality issues.

Pro tip: Always confirm the grain of the data and whether 'views' and 'likes' are cumulative or per-story; this affects aggregation. Also, consider using named aggregations for clarity and performance.

1. Clarify requirements and data schema

Ask about the dataset columns, definitions of 'group story', 'story type', and how views/likes are recorded. Confirm the output format expected.

2. Filter and count group stories per user

Use pandas to filter rows where story type is 'group', then group by user ID and count stories. Filter users with count >= 3.

3. Aggregate views and likes by story type

Group the original dataset by story type and sum views and likes. Ensure to handle any missing values appropriately.

4. Validate and present results

Check for duplicates, data quality issues, and verify counts. Present the two results clearly, possibly using separate DataFrames or a combined summary.

Key Points to Mention

Data schema and definitions (e.g., what is a 'group story', how views/likes are stored)
Use of groupby and filter for user story counts
Aggregation functions (sum, count) and handling of missing data
Potential data quality issues (duplicates, inconsistent story types)
Efficiency considerations for large datasets (e.g., using vectorized operations)
Clear separation of the two tasks and validation of results

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