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Plymouth Rock Assurance Corporation·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

SQL question for a Data Scientist role at Plymouth Rock Assurance Corporation, insurance domain flavor. One question, window function territory, not a bad problem if you've worked with cumulative aggregations before.

Questions Asked (1)

Q1

Given a table of insurance policy term losses, write a SQL query that computes the 'ultimate loss' for each policy term, defined as the sum of loss amounts for that term and all subsequent terms within the same policy.

Data ModelingAlgorithms & Data Structures
Author's notes

The key is recognizing this is a reverse cumulative sum partitioned by policy.

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AI HintsAI Generated

Suggested Approach

Clarify the table schema and the definition of 'subsequent terms' (e.g., by term number or date). Then use a window function like SUM() OVER (PARTITION BY policy_id ORDER BY term_id ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) to compute the cumulative sum from the current term to the last term for each policy.

Pro tip: Mention that in insurance, 'ultimate loss' often includes IBNR and development factors, but for this SQL exercise, focus on the cumulative sum and confirm the ordering column. Also, consider performance implications for large datasets and suggest indexing on (policy_id, term_id).

1. Understand the data and requirements

Identify the table columns: policy_id, term_id (or term date), and loss_amount. Confirm that 'subsequent terms' means terms with a higher term_id or later date within the same policy.

2. Choose the right SQL technique

Use a window function to compute a running total from the current row to the end of the partition. Alternatively, a self-join or correlated subquery can work but may be less efficient.

3. Write the query with window function

Construct: SELECT policy_id, term_id, loss_amount, SUM(loss_amount) OVER (PARTITION BY policy_id ORDER BY term_id ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) AS ultimate_loss FROM table;

4. Validate and handle edge cases

Check for ties in term_id (use additional ordering if needed), null loss amounts (treat as 0), and ensure the result is correct for the last term (should equal its own loss).

5. Optimize and explain

Discuss indexing on (policy_id, term_id) for performance. Explain the window frame and why it computes the desired cumulative sum.

Key Points to Mention

  • Window functions (SUM OVER) with PARTITION BY and ORDER BY
  • Frame specification: ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
  • Handling ties in ordering column (e.g., using term_id and another column)
  • Performance considerations: indexing, avoiding self-joins for large data
  • Edge cases: nulls, single-term policies, and ensuring correct ordering
  • Alternative approaches: self-join or correlated subquery, and their trade-offs

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