CVS Health·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Technical screen for a Data Scientist role at CVS Health where they went deep on a past ML project. The whole thing was basically one long question about causal inference, which I was not fully prepared to narrate cleanly under pressure.

Questions Asked (1)

Walk me through an ML project you led end-to-end, including any causal inference methods you used along the way.

A/B Testing & ExperimentationTechnical Trade-offsProduct Analytics & Metrics

Author's notes

I started with the model and worked backwards, which was a mistake.

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AI HintsAI Generated

Suggested Approach

Choose a project where you had clear ownership from problem definition to deployment, and structure your answer around the end-to-end ML lifecycle. Highlight where causal inference was critical—such as estimating treatment effects from observational data or validating A/B test results—and explain how you ensured rigor and business impact.

Pro tip: Emphasize how you balanced causal validity with practical constraints (e.g., data limitations, time, or stakeholder needs), and quantify the business impact of your causal findings to show you connect methodology to outcomes.

1. Define the problem and causal question

Start by describing the business problem and the specific causal question you aimed to answer (e.g., 'What is the effect of a medication adherence program on hospital readmissions?'). Explain why causal inference was necessary rather than just predictive modeling.

2. Data collection and causal design

Detail the data sources (e.g., claims, EHR, engagement logs) and how you addressed confounding, selection bias, and other threats to validity. Mention the causal inference method you chose (e.g., propensity score matching, difference-in-differences, instrumental variables) and why it fit the context.

3. Modeling and validation

Explain how you implemented the causal model, including any sensitivity analyses or robustness checks (e.g., placebo tests, negative controls). Discuss how you validated assumptions (e.g., parallel trends, overlap) and handled practical challenges like missing data or unmeasured confounding.

4. Deployment and integration

Describe how you operationalized the model or findings—whether by informing a business strategy, powering a dashboard, or integrating into a production system. Highlight collaboration with engineering, product, or clinical teams.

5. Impact measurement and iteration

Share the measured impact (e.g., lift in key metric, cost savings) and how you monitored performance over time. Mention any follow-up experiments or refinements based on results.

Key Points to Mention

Clear problem framing and why causal inference was needed (e.g., to inform a decision, not just predict).
Choice of causal method (e.g., propensity score matching, difference-in-differences, instrumental variables) and justification.
Handling of confounding and bias, including sensitivity analyses and robustness checks.
Integration of causal findings with A/B testing or experimentation to validate results.
Cross-functional collaboration (e.g., with clinicians, product managers, engineers).
Quantified business impact (e.g., increased adherence by X%, reduced costs by Y%).

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