CVS Health

Large Enterprises

Most of these CVS Health write-ups come from Data Scientists (24), then Software Engineers (2). The most reported rounds are the technical phone screen (21), the behavioral / leadership round (2), the hiring manager screen (1) and the recruiter screen (1).

26 interview notes · updated Jul 2026

CVS Health·Data Scientist·Technical Phone Screen

Jul 2026

CVS Health data scientist interview with a deep-dive case question on diagnosing a failed vaccination lift experiment. The question was layered enough that I kept second-guessing whether I was going too deep or not deep enough.

Your vaccination outreach experiment shows no lift overall and a negative effect in the senior population. Walk through how you'd diagnose what went wrong, from exposure verification to re-test design, and explain what data you'd pull and what you'd actually do with it.

“This one wrecked me a little.”

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CVS Health·Data Scientist·Technical Phone Screen

Jul 2026

CVS Health data scientist interview that was basically one giant case study about designing a geo-experiment for a TV flu vaccination campaign. Dense question, lots of moving parts, felt like a take-home crammed into a conversation.

You need to plan and measure a 6-week linear TV campaign aimed at boosting flu vaccinations. Walk through how you'd select 12 test DMAs and matched control markets from the full set of 210 U.S. DMAs, what your matching criteria would be, how long a pre-period you'd use, and how you'd protect the experiment from noise like news cycles, sports events, or holidays.
Define the KPIs and causal design for this experiment. How would you handle market spillover, unequal TRP delivery across DMAs, and concurrent media running at the same time?
Describe your GRP and TRP targets, daypart mix, and reach-frequency goals for the campaign. How would you model adstock decay and saturation effects?
How would you run a power calculation for this geo-experiment given market-level variance? What guardrails would you set up, and how would you triangulate your results with marketing mix modeling and pharmacy footfall data?

“This is where I spent most of my time and still felt like I only got halfway through.”

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CVS Health·Data Scientist·Technical Phone Screen

Jul 2026

SQL-heavy technical screen for a Data Scientist role at CVS Health. Three interconnected queries on a healthcare claims schema, each building on the last. The kind of interview where you realize mid-question that the join order you chose two minutes ago is going to cause you pain.

Using a schema with member, claim, and geography tables, write a query to return the 2020 sum of paid_amt grouped by age_band across all states. Age bands with zero 2020 spend should still appear as 0.00, and rows with NULL paid_amt should be excluded.
Extend the previous query to compute each age band's percentage share of the total 2020 paid amount. Output should be 0 to 100 with two decimal places, and the query must handle division by zero safely.
For Georgia members only, compute 2019 and 2020 paid totals and the year-over-year absolute and percentage change for the 44-65 and 65+ age bands. Members must actually reside in Georgia; claims from non-GA members should be excluded even if the claim data could be linked.

“The trick here is join order.”

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CVS Health·Data Scientist·Recruiter / HR Screen

Jul 2026

Standard HR screen for a Data Scientist role at CVS Health, just the usual gatekeeping stuff before they decide whether to pass you along to technical rounds. Nothing surprising, but worth being prepared.

What is your current work authorization status?
Walk me through your professional background and how it relates to this role.
What's motivating you to look for a new position right now?

“Straightforward compliance check.”

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CVS Health·Data Scientist·Technical Phone Screen

Jun 2026

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.

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

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

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CVS Health·Data Scientist·Technical Phone Screen

Jun 2026

CVS Health data scientist interview with a heavy stats focus, basically a self-contained hypothesis testing case study. Five parts, all connected, and the power calculation at the end is the kind of thing you either know cold or you're winging it in real time.

Given a before/after payment accuracy experiment, choose an appropriate hypothesis test and formally state the null and alternative hypotheses.
Compute the test statistic and two-sided p-value for the difference in proportions between treatment and control. Also explain when you'd use a pooled versus unpooled standard error and justify your choice here.
Build a 95% confidence interval for the difference in proportions (treatment minus control) using the experiment data.
Summarize the experimental results in a single sentence for a non-technical stakeholder, and address whether the improvement is practically significant.
Using the normal approximation, calculate the per-group sample size needed to detect an absolute improvement from 96.0% to 96.2% accuracy with 80% power at a two-sided alpha of 0.05. Show the formula and plug in the Z-scores explicitly.

“Pretty standard setup once you recognize it's two proportions.”

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CVS Health·Data Scientist·Technical Phone Screen

Jun 2026

CVS Health data scientist technical screen, PostgreSQL heavy. Five distinct tasks covering schema design, data insertion, aggregation queries, date windowing, and a DML update. No behavioral stuff at all, just straight SQL the whole time.

Design a normalized e-commerce schema in PostgreSQL with products, customers, orders, and order_items tables. Use minimal correct types and include appropriate constraints like primary keys, foreign keys, NOT NULL, UNIQUE, and CHECK constraints.
Write a query to compute per-customer gross revenue for paid orders placed in August 2025. Include customers with zero revenue as 0, and sort results by revenue descending then customer_id ascending.
List all products that appear in non-refunded, non-cancelled orders placed within the last 7 days relative to a fixed reference date of September 1, 2025. Return the product id, name, and the earliest order date within that window.
Write an UPDATE statement that sets coupon_code to 'NONE' for all paid orders from August 2025 where coupon_code is currently NULL. Then write a SELECT to verify the count of affected rows.
Given ASCII table samples, write INSERT statements that exactly reproduce the provided data across all four tables, respecting foreign key relationships and insertion order.

“The 'minimal correct types' phrasing tripped me up a bit.”

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CVS Health·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Interviewed for a Data Scientist role at CVS Health. The question was a layered behavioral prompt that also doubled as a strategy question, which I didn't fully see coming.

What are your top one or two strengths most relevant to this role, and can you walk me through a concrete example that shows measurable impact? Also, what trade-offs did you make, how did you bring other teams along, what would you do differently, and how would you apply those strengths in your first 90 days on our experimentation and measurement work?

“This is five questions stitched into one and I did not clock that fast enough.”

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CVS Health·Data Scientist·Technical Phone Screen

Jun 2026

CVS Health data scientist interview with a meaty causal inference case study around flu-shot campaign targeting. The whole thing was essentially one long open-ended problem covering modeling, evaluation, policy design, and online testing. Pretty intense for a single session.

You have historical campaign logs with randomized holdouts. How would you design a treatment effect modeling approach to decide who to contact via SMS or Email for an upcoming flu-shot campaign, given demographic features, past vaccination history, engagement signals, and known treatment assignment probabilities?
How would you evaluate uplift models offline? Walk through the metrics you'd use and how you'd factor in channel costs like SMS at $0.02 versus Email at $0.001.
Given a budget that allows contacting at most 40% of eligible customers, how would you rank customers and assign them to a channel?
How would you design an online validation test to compare model-based targeting against uniform random targeting? What success metrics and stopping rules would you define?
How would you detect segments where your model is actually causing harm, meaning negative uplift, and what would you do about them in the targeting policy?

“This is where I spent most of my mental energy.”

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CVS Health·Data Scientist·Technical Phone Screen

Jun 2026

CVS Health data scientist interview, technical round focused entirely on SQL. One long multi-part problem covering deduplication, window functions, and distribution bucketing. Pretty dense for a single question but it covered a lot of ground.

You have two tables with intentional duplicates. Write SQL to: (a) identify duplicate user_ids and produce a deduplicated users table using window functions, picking the canonical row by earliest signup date then alphabetically by name; (b) find users with at least 2 purchases after deduplicating orders so each order_id counts once; (c) build a distribution table of purchase counts per user including zeros, with each bucket's percentage of total unique users.

“This was one question but it felt like three separate interviews stapled together.”

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CVS Health interview FAQ

What rounds are in the CVS Health interview?

Candidates most often report a technical phone screen (21 write-ups), a behavioral / leadership round (2), a hiring manager screen (1) and a recruiter screen (1).

What does the CVS Health technical phone screen cover?

Across 21 technical phone screen write-ups, the most tagged question topics are Product Analytics & Metrics (49 questions), A/B Testing & Experimentation (31) and Data Modeling (22).