CVS Health·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

CVS Health data scientist interview that was basically one massive MMM question dressed up as a technical screen. Whoever wrote this prompt clearly wanted to see if you could hold a lot of moving parts in your head at once, because the question covered everything from multicollinearity to privacy signal loss to geo-test calibration without letting up.

Questions Asked (1)

You're handed a 156-week marketing mix model with TV GRPs, paid search, display, email, price, promotions, and a competitor index. TV and search are correlated at 0.90 and there's a structural break around week 70 due to the pandemic. What makes this model fragile, and how do you fix it?

Data ModelingTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This was basically the whole interview in one question.

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AI HintsAI Generated

Suggested Approach

Start by diagnosing the specific fragilities: multicollinearity between TV and search, non-stationarity from the structural break, and potential confounding from price/promotions. Then propose a multi-pronged fix: regularization or dimension reduction for collinearity, piecewise modeling or time-varying coefficients for the break, and causal inference methods to isolate true media effects.

Pro tip: Don't just list problems—quantify the impact. For example, mention that a 0.90 correlation inflates standard errors by a factor of ~2.3, making TV and search coefficients unreliable. Then show you can prioritize fixes based on business impact.

1. Diagnose Fragility

Identify and quantify the key issues: multicollinearity (VIF > 5 for TV and search), structural break (Chow test or CUSUM), and potential omitted variable bias from price/promotions.

2. Address Multicollinearity

Apply regularization (ridge/lasso) or dimension reduction (PCA) to stabilize coefficients, or use hierarchical Bayesian models with informative priors to separate TV and search effects.

3. Handle Structural Break

Model pre- and post-pandemic periods separately, or include time-varying coefficients (e.g., state-space models) to allow media effectiveness to shift over time.

4. Control for Confounders

Incorporate price, promotions, and competitor index appropriately, and consider causal methods (e.g., instrumental variables, synthetic control) to isolate media effects from endogenous marketing decisions.

5. Validate and Iterate

Use out-of-sample validation (e.g., holdout weeks) and sensitivity analyses to ensure the model is robust and generalizes beyond the observed period.

Key Points to Mention

Multicollinearity inflates standard errors and makes TV/search coefficients unstable and hard to interpret.
Structural break invalidates the assumption of constant parameters; pre- and post-pandemic effects may differ significantly.
Regularization (ridge/lasso) or Bayesian priors can mitigate multicollinearity but require careful tuning.
Time-varying coefficient models or piecewise regression can capture the structural break.
Endogeneity of price and promotions: they are often set in response to sales, so need causal methods like instrumental variables.
Validation should include out-of-sample testing and checking for parameter stability over time.

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