CVS Health Interview Questions — Page 1

CVS Health·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at CVS for a data science role, just the one question from what I can tell. Pretty standard stuff but it made me think more than I expected.

Describe a situation where you had to weigh immediate results against longer-term objectives.

“I rambled a bit here.”

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CVS Health·Data Scientist·Technical Phone Screen

Apr 2026

CVS Health data scientist interview that was basically a pandas gauntlet. Four connected tasks on the same merged DataFrame, each building on the last. More hands-on than I expected for a DS role at a health company.

Given two DataFrames (users and orders), merge them on user_id with a left join, then compute total delivered revenue grouped by channel, and separately compute delivered revenue by channel for members only.
Using the merged DataFrame, build a 2x2 pivot table of delivered revenue with membership status as rows and channel (SMS and Email only) as columns, filling missing values with 0.
From the merged DataFrame, compute per-state total order count and count of unique purchasers, then return the top 2 states by total orders.
Add a binary flag column to the DataFrame: 1 if a user's total lifetime delivered amount is at least 15 OR they have 2 or more delivered SMS orders, otherwise 0. Use np.where and avoid SettingWithCopyWarning.

“Pretty standard groupby stuff but the 'members only' filter tripped me up slightly because I initially filtered before merging instead of after.”

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CVS Health·Data Scientist·Technical Phone Screen

Apr 2026

CVS Health data scientist interview that was basically a stats exam. Four-part A/B testing problem covering hypothesis testing, confidence intervals, sample size planning, and multiple comparisons correction. Felt more like a grad school problem set than a conversation.

Given an A/B test with 520 conversions out of 10,000 sessions for control and 630 conversions out of 11,500 sessions for variant, compute the pooled-proportion z-statistic and the two-sided p-value for a two-proportion z-test.
Using the same A/B test data, compute a 95% confidence interval for the difference in conversion rates (pB minus pA) using the unpooled standard error.
Using the baseline conversion rate from the control group, what per-arm sample size is needed to detect an absolute lift of 0.7 percentage points at 80% power and two-sided alpha of 0.05? State the formula and round up.
If you were simultaneously tracking 12 independent metrics in this experiment, what per-metric significance threshold would you apply to control the family-wise error rate at 0.05 using Bonferroni correction?

“The pooled proportion part tripped me up slightly.”

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CVS Health·Data Scientist·Technical Phone Screen

May 2026

CVS Health data scientist interview that leaned heavily on applied stats. Three questions, all quantitative, no fluff about your background or why you want the job. If your stats fundamentals are rusty this will hurt.

You have a sample of 64 observations from a normal population with unknown variance, a sample mean of 102, and a sample standard deviation of 16. Construct a 95% confidence interval for the population mean. State the formula you use and the degrees of freedom for your t critical value.
You measure two variables on 100 observations and get a Pearson correlation of 0.25. Test whether the true population correlation is zero at the 0.05 significance level. Show your test statistic, degrees of freedom, the two-sided p-value, and your conclusion.
A process follows a normal distribution with mean 50 and standard deviation 5. Find the value t such that only the top 2.5% of items exceed it, and explain what that means in context.

“Pretty standard but the unknown variance part is the key detail.”

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CVS Health·Data Scientist·Technical Phone Screen

May 2026

CVS Health data scientist interview with a single massive ML design question covering missing data, class imbalance, metrics, and logistic regression improvements all at once. The depth expected was pretty intense for what I assumed would be a standard technical screen.

You're building a binary classifier to predict 30-day hospital readmission using claims and EHR data, with ~30% missing values and only 6% positive class rate. Walk through your full plan: how do you diagnose and handle missing data, choose and justify classification metrics, deal with class imbalance, and improve logistic regression beyond the baseline, all while preventing leakage?

“This was basically four questions stapled together and I did not realize that until I was already three minutes into the missing data section.”

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CVS Health·Data Scientist·Technical Phone Screen

May 2026

CVS Data Scientist technical screen, pretty much all SQL and Pandas. Three parts back to back, no behavioral fluff, just get in and write code. Felt more like a take-home crammed into a live session.

Given a claims table and a member table split across two sources, write a SQL query to compute the total paid amount per specialty and each specialty's share of the overall total paid.
Filter claims to calendar year 2017, union two member tables together, join to claims, and return the age band or bands with the highest claim count, including ties.
In Pandas, recode a gender column from M/F to male/female, then compute total paid amount grouped by gender and month for the year 2017.

“Window function territory.”

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CVS Health·Software Engineer·Hiring Manager Screen

May 2026

Behavioral screen for a bizops role at CVS. Just one question but it was the whole conversation basically, they really dug into the details.

Tell me about a time when a program you were managing fell behind on its committed timelines. What did you do?

“I had a real example ready but fumbled the sequencing a bit.”

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CVS Health·Data Scientist·Technical Phone Screen

May 2026

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.

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?

“This was basically the whole interview in one question.”

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CVS Health·Data Scientist·Technical Phone Screen

May 2026

A case-style technical round at CVS Health for a Data Scientist role. The whole thing was basically one long scenario about a payment accuracy incident, and they wanted a structured response covering triage, root cause analysis, mitigation, comms, and prevention. Felt more like a staff-level system design session than a typical DS interview.

You're the on-call lead and discover that incorrect payment rates for 18-24 year olds in CA jumped from 2% to 3.1% across ~120k transactions overnight. Finance wants a mitigation in 24 hours. Walk through how you'd triage and quantify the impact, including who owns what and what 'all clear' looks like.
How would you form and test hypotheses to isolate the root cause? What specific data slices, queries, or holdout comparisons would you run first?
How do you decide on a safe mitigation, and what rollback triggers would you set? Be specific about the precision/recall trade-offs you'd accept.
Walk through your communication plan to executives and partner teams. What artifacts do you send, and at what times?
What would you put in place after this incident to prevent recurrence? Think observability, guardrails, and pre-deploy checks.

“This was the part I felt least prepared for.”

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CVS Health·Data Scientist·Take-home Assignment

May 2026

CVS Health data scientist interview that was basically a coding session disguised as a take-home. Two fairly involved problems: one on implementing R-squared from scratch and another on PCA with and without standardization. More math-heavy than I expected for a health company role.

Implement an r2_score function using only NumPy (no scikit-learn) that handles edge cases like perfect predictions, zero-variance targets, and division by zero. Test it on a standard case and two edge cases where all true values are identical.
Given a 6x3 matrix with columns on very different scales, run PCA twice: once on raw data and once on column-standardized data. For each, compute the covariance matrix via NumPy, sort eigenvectors by eigenvalue, report explained variance ratio for the first two components, and discuss why scaling changes the principal components.

“The normal case was fine, SS_res over SS_tot, nothing crazy.”

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