CVS Health Data Scientist candidates commonly report SQL and Pandas work, statistics and machine-learning discussion, business cases, and behavioral interviews across two to three rounds.
$141K
Avg. Base Comp
$173K
Avg. Total Comp
2-3 rounds
Typical Rounds
2-4 weeks
Process Length
CVS Health Data Scientist interviews reported by candidates center on practical analysis rather than algorithm-heavy coding. SQL and Pandas are recurring early technical themes: candidates describe joins, CTEs, aggregations, filtering, window functions, duplicate detection, and translating data operations between SQL and Pandas. One account also required showing the output of SQL and Pandas work before moving on, so practice explaining both query logic and expected results.
The applied portion often moves from technical mechanics to judgment. Candidates report classification and business case discussions covering feature choices, class imbalance, model tradeoffs, evaluation, and how to structure an A/B test. Statistics and probability also appear in technical rounds. Prepare to connect a method to a business decision rather than simply naming an algorithm.
Later conversations may be behavioral or case-based. Reports include questions about prior experience, while another candidate described a final VP discussion built around applying ML knowledge to a business use case. The evidence does not establish a universal sequence: candidates reported different opening steps and round structures, and some encountered an assessment before interviews. Build concise examples of how you reasoned through a data problem, communicated findings, and handled tradeoffs.
Synthesized from 7 candidate reports by our editorial team.
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Real interview reports from people who went through the Cvs Health process.
This was for a contract position and consisted of 3 rounds from CVS 1) In the first round, I was tested on two SQL (medium - difficult level) questions and two Pandas (Easy-Medium level). I was asked to show the correct output before moving on. Basic ML questions were also asked. 2) Second round was Case Study, where I was given a business problem, and was asked to provide a solution for it. They checked if I was going in the right direction or not. There is no perfect answer for these case studies. 3) Third was a behavioral questions round, where I was asked two questions about my previous experiences.
Questions asked: Overall, the interview wasn’t meant to be super algorithm-heavy like a software engineering interview. It was much more focused on practical data analysis, SQL, Python (especially pandas), statistics, and how I think through business problems. I only remember one SQL question: The first was a straightforward aggregation question.
You have a table of prescriptions with:
Return the total amount spent by each patient.
Pretty basic GROUP BY patient_id.
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Topics based on recent interview experiences.
Featured question at Cvs Health
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Rain in N Days | |
| Button AB Test | |
| Flight Records | |
| Always Excited Users | |
| Bagging vs Boosting | |
| RMS Error | |
| Cumulative Reset | |
| Detecting ECG Tachycardia Runs | |
| Size of Joins | |
| Brain Cancer Treatment Outcomes | |
| Total Transactions | |
| Using R Squared | |
| Random Forest Explanation | |
| Causal Email Journey | |
| Valid Anagram | |
| Percentage of Revenue by Year | |
| Xgboost vs Random Forest | |
| Lasso vs Ridge | |
| Assumptions of Linear Regression | |
| Duplicate Rows | |
| Count Transactions | |
| Type I and II Errors | |
| Data Preparation for Imbalanced Data | |
| A/B Testing a Checkout Button Change | |
| Overfit Avoidance | |
| SageMaker Deployment Architecture | |
| Testing Constraints | |
| Seller Type Modeling | |
| Why Do You Want to Work With Us |
Synthesized from candidate reports. Individual experiences may vary.
Some candidates reported an HR screen focused on profile alignment, while another began with an SHL assessment covering basic Python, SQL, statistics, and probability. Treat this as a possible opening rather than a universal first round.
Candidates frequently report SQL and Pandas exercises, including joins, CTEs, aggregations, filtering, window functions, duplicate detection, and a left join to identify NULLs. One candidate also described debugging Python and writing an LLM-response test case.
Candidates report classification and business-case discussions involving model tradeoffs, feature selection, class imbalance, evaluation, and A/B testing. Technical conversations may also cover statistics and probability, including hypothesis testing and Type I and Type II errors.
A final conversation may be behavioral, with questions about prior experience, or case-based with a senior leader. One candidate described a VP round applying ML knowledge to a business use case; another reported a behavioral third round.
