Capital One·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Capital One data scientist interview, technical screen focused entirely on a single meaty SQL problem with sample data. The question looked straightforward at first glance but the returns logic and tie-breaking rules added enough wrinkles to slow me down.

Questions Asked (1)

Given a fixed 7-day window (2025-08-26 to 2025-09-01), write a single SQL query that finds the top product by net revenue within each category. Net units must subtract returns that fall in the same window, and ties should be broken by net units then lexicographic product ID. Use window functions to pick one winner per category. Output category, product_id, net_units, and net_revenue_usd.

Data ModelingProduct Analytics & MetricsAlgorithms & Data Structures

Author's notes

The returns join is where I fumbled.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and assumptions (e.g., separate sales and returns tables, date fields, product-category mapping). Then build a CTE that aggregates net units and net revenue per product within the fixed window, and finally use a window function (ROW_NUMBER) partitioned by category with the specified ordering to select the top product per category.

Pro tip: Explicitly state your assumptions about the data model (e.g., returns are in a separate table with negative units or positive units to subtract) and confirm them with the interviewer before writing SQL. This shows you think about data quality and edge cases, which is crucial for a data scientist at a bank.

1. Clarify schema and assumptions

Ask about table structures, column names, date fields, and how returns are represented (e.g., separate table, negative values). Confirm the definition of net units and net revenue.

2. Aggregate net metrics per product

Write a CTE that computes net_units and net_revenue_usd per product by summing sales and subtracting returns within the fixed window (2025-08-26 to 2025-09-01).

3. Rank products within each category

Use a window function like ROW_NUMBER() OVER (PARTITION BY category ORDER BY net_revenue_usd DESC, net_units DESC, product_id ASC) to assign a rank to each product.

4. Select the top product per category

Filter the ranked results to only include rows where the rank equals 1, and output the required columns: category, product_id, net_units, net_revenue_usd.

Key Points to Mention

Use of window functions (ROW_NUMBER or RANK) with PARTITION BY category and multi-level ORDER BY for tie-breaking.
Handling of returns: ensure returns are subtracted correctly, possibly using LEFT JOIN or UNION ALL with negative values.
Date filtering: apply the fixed window to both sales and returns, and consider time zones if applicable.
Aggregation: sum net units and net revenue per product before ranking.
Tie-breaking logic: order by net_revenue_usd DESC, then net_units DESC, then product_id ASC (lexicographic).
Performance considerations: filter early, use appropriate indexes, and avoid unnecessary subqueries.

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