CVS Health·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

CVS Health data scientist interview, technical round focused entirely on SQL. One long multi-part problem covering deduplication, window functions, and distribution bucketing. Pretty dense for a single question but it covered a lot of ground.

Questions Asked (1)

You have two tables with intentional duplicates. Write SQL to: (a) identify duplicate user_ids and produce a deduplicated users table using window functions, picking the canonical row by earliest signup date then alphabetically by name; (b) find users with at least 2 purchases after deduplicating orders so each order_id counts once; (c) build a distribution table of purchase counts per user including zeros, with each bucket's percentage of total unique users.

Data ModelingProduct Analytics & MetricsAlgorithms & Data Structures

Author's notes

This was one question but it felt like three separate interviews stapled together.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Break the problem into three clear parts: deduplication, aggregation, and distribution. For deduplication, use ROW_NUMBER() with a window partitioned by user_id and ordered by signup_date ASC, name ASC, then filter for row number 1. For the purchase count, deduplicate orders first (e.g., using DISTINCT or ROW_NUMBER() on order_id), then count purchases per user and filter for count >= 2. For the distribution, generate a series of buckets (0, 1, 2, 3+), left join with user purchase counts, and compute percentages over total unique users.

Pro tip: Always clarify the definition of a 'purchase' and how to handle ties in deduplication—interviewers value candidates who proactively address edge cases and data quality assumptions.

1. Deduplicate users

Use ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY signup_date ASC, name ASC) to assign a rank, then select rows where rank = 1 to get the canonical user record.

2. Deduplicate orders

Ensure each order_id is counted once by using DISTINCT or ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY ...) and filtering for rank = 1.

3. Count purchases per user

Join deduplicated orders with deduplicated users on user_id, then GROUP BY user_id and COUNT(order_id) to get purchase counts.

4. Filter users with >=2 purchases

Apply a HAVING clause to the grouped result to select only users with purchase count >= 2.

5. Build distribution with zeros

Create a bucket list (0, 1, 2, 3+), left join with user purchase counts (including users with zero purchases), and compute each bucket's percentage of total unique users.

Key Points to Mention

Use of window functions like ROW_NUMBER() for deduplication with deterministic ordering.
Handling ties in deduplication by specifying secondary sort criteria (e.g., name alphabetically).
Ensuring orders are deduplicated by order_id before counting purchases.
Using LEFT JOIN or a calendar/bucket table to include users with zero purchases in the distribution.
Calculating percentages as bucket count divided by total unique users, ensuring the sum equals 100%.
Considering performance implications and indexing on join keys for large datasets.

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