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Amazon·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Amazon Data Scientist interview with a combined SQL and Python analytics question built around an e-commerce sales dataset. The problem was scoped around Prime vs non-Prime segmentation and price bucketing, which felt very on-brand for the team.

Questions Asked (1)

Q1

Given a sales table with order date, a Prime membership flag, and price, write an SQL query that returns total sales broken out by Prime vs non-Prime customers for each of the past 12 months. Then, using pandas, compute the percentage of monthly sales contributed by each price bucket, where you define the bucket boundaries yourself.

Product Analytics & MetricsData Modeling
Author's notes

The SQL part was fine, month truncation plus a GROUP BY and a conditional aggregate.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and assumptions (e.g., date range, price definition). Write a SQL query that aggregates total sales by Prime flag and month for the last 12 months, then use pandas to bucket prices and compute the percentage contribution of each bucket to monthly sales.

Pro tip: Mention that you would validate the SQL results against a quick pandas groupby to ensure consistency, and discuss how you'd handle edge cases like missing months or price outliers.

1. Clarify requirements and assumptions

Confirm the table schema, define 'past 12 months' (e.g., relative to current date or max date in table), and decide on price bucket boundaries (e.g., quartiles or fixed ranges).

2. Write SQL for monthly sales by Prime status

Use a GROUP BY on month and Prime flag, filtering for the last 12 months, and SUM(price) as total sales. Ensure date truncation to month.

3. Load SQL results into pandas

Execute the query and read results into a DataFrame with columns: month, prime_flag, total_sales.

4. Bucket prices and compute percentages

If raw transaction data is available, create price buckets using pd.cut, then group by month and bucket to sum sales. Compute each bucket's percentage of total monthly sales.

5. Present and validate results

Show the final table with monthly percentages per bucket, and discuss validation (e.g., percentages sum to 100% per month) and potential insights.

Key Points to Mention

  • Use DATE_TRUNC or equivalent to group by month in SQL.
  • Filter for the last 12 months using a date condition (e.g., WHERE order_date >= DATEADD(month, -12, CURRENT_DATE)).
  • Define price buckets clearly (e.g., quartiles, fixed ranges) and justify the choice.
  • In pandas, use groupby and transform to calculate percentage of monthly total.
  • Handle missing months or zero sales to avoid division by zero.
  • Validate that percentages sum to 100% for each month.

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