CVS Health Data Scientist candidates report a two-to-three-round process with SQL, Python or Pandas, statistics, machine learning, case discussion, and occasional behavioral or HR conversations.
$101K
Avg. Base Comp
$180K
Avg. Total Comp
2-3 rounds
Typical Rounds
2-4 weeks
Process Length
CVS Health Data Scientist interviews in these reports combine practical data work with applied analytical reasoning. SQL is the most consistently reported technical area. Candidates described joins, NULL handling with a LEFT JOIN, duplicate detection, CTEs, aggregation, filtering, subqueries, and window functions. One report paired SQL with Pandas and emphasized translating logic between the two, including cumulative calculations.
Prepare for quantitative questions beyond query writing. Reported technical conversations covered statistics, probability, hypothesis testing, Type I and Type II errors, machine-learning theory, and classification-model tradeoffs. In one ML case, the discussion included feature choice, class imbalance, evaluation methodology, and how to design an A/B test through metrics, hypotheses, randomization units, power analysis, and a scale-up decision.
The reported sequence varies. One candidate had an HR screen followed by a CoderPad technical round with SQL and Python debugging. Other candidates described three rounds, including two technical rounds and a behavioral close; another described an assessment, a technical interview, and a final VP case discussion. Build practice around clear reasoning: write and explain SQL, work through a small Python task, and connect an ML or experimentation approach to a business question.
Synthesized from 6 candidate reports by our editorial team.
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Featured question at Cvs Health
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Rain in N Days | |
| Flight Records | |
| Button AB Test | |
| Always Excited Users | |
| Bagging vs Boosting | |
| RMS Error | |
| Cumulative Reset | |
| Size of Joins | |
| Detecting ECG Tachycardia Runs | |
| Brain Cancer Treatment Outcomes | |
| Using R Squared | |
| Random Forest Explanation | |
| Percentage of Revenue by Year | |
| Valid Anagram | |
| Causal Email Journey | |
| 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 | |
| Xgboost vs Random Forest | |
| Justify a Neural Network |
Synthesized from candidate reports. Individual experiences may vary.
Reported starting points include an HR screen and an SHL assessment. The HR screen focused on profile alignment and general application details. The assessment included basic Python tasks such as normalization, standardization, and RMSE, along with basic SQL, statistics, and probability questions. The candidate who described the assessment noted that the time allowed for each question was limited. These reports support preparing a concise introduction as well as refreshing core quantitative and coding fundamentals before the technical interview.
Technical interviews included SQL joins, finding NULL values with a LEFT JOIN, duplicate detection, CTEs, aggregation, filtering, subqueries, and window functions. Candidates also described Pandas data manipulation and a CoderPad round that included Python debugging and writing a test case to check an LLM response. Statistics and probability appeared in technical rounds as well. Practice explaining your query logic and checking assumptions, rather than treating the task as a syntax-only exercise.
Later discussions can test machine-learning and business reasoning. One candidate worked through a classification problem, comparing logistic regression, random forest, and XGBoost; discussing features, class imbalance, and evaluation; then outlining an A/B test from metrics through a scale-up decision. Other reports mention ML theory, hypothesis testing, an in-depth VP case applying ML to a business use case, and a behavioral final round. Be ready to state tradeoffs clearly and connect your technical recommendation to the problem at hand.