Intuit Data Scientist Interview Questions

Intuit·Data Scientist·Technical Phone Screen

Jul 2026

A fairly intense technical screen for a DS role at Intuit, centered entirely on one long delivery cost case study. The question had a lot of moving parts and I'm not sure I covered everything they were looking for.

A restaurant sees a significant spike in delivery cost per order over the past month. Given tables for orders, courier payments, platform fees, weather, and store operations, how would you first confirm the metric increase is real and not an artifact of how the metric is defined or calculated?
Walk through how you would decompose the increase in delivery cost per order into its component drivers, covering mix effects like platform, distance, zone, and time of day, as well as rate effects like pay per km or surge multipliers, and execution issues like cancellations or SLA breaches.
What causal hypotheses would you consider for why delivery costs increased, and how would you quantify each one? For example, could weather shocks, staffing changes that increase courier wait times at pickup, or platform policy changes be responsible?
Propose two concrete experiments you would run to reduce delivery cost per order. What would your success metrics and guardrails be, and how would you think about statistical power and potential sources of bias?
What are the most common pitfalls when analyzing delivery cost per order in this setup, and how would you guard against each one?

“This part tripped me up more than I expected.”

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Intuit·Data Scientist·Technical Phone Screen

Jul 2026

SQL-heavy technical screen for a Data Scientist role at Intuit. The problem was a single multi-part question built around a subscription analytics table, escalating from basic cohort aggregation to YoY growth rates to a paid-only filter variant. Felt more like a take-home in spirit but was done live.

Given a user-level table with signup and subscription dates, write a query that returns, for each calendar month of signups, the total signups, the count who ever subscribed, and the conversion rate.
Extend the query to compute year-over-year subscription rate growth for each month, defined as the percentage change in conversion rate versus the same calendar month in the prior year. Return NULL if the prior year's month isn't in the data.
Now assume the table includes both free and paid subscription types. Recompute the YoY growth metric but treat only paid subscribers as 'converted'. How does your query change?

“The cohort grouping part was fine, DATE_TRUNC on signup_date and a conditional count on subscription_date not being null.”

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Intuit·Data Scientist·Technical Phone Screen

Jul 2026

Intuit data scientist interview that went deep on A/B testing statistics, like really deep. They wanted full derivations, not just 'run a z-test and move on,' so if you're fuzzy on where the z-test formula actually comes from, this round will find that out fast.

For an A/B test on a binary delivery outcome, which significance test would you use and why is it preferable to a t-test on proportions? Derive the test statistic from first principles starting from the Bernoulli distribution through to the normal approximation, and state the conditions under which that approximation holds.
Using the large-sample A/B test data (control: 920/1000 delivered, variant: 880/1000 delivered), compute the p-value to three significant figures and a 95% confidence interval for the difference in proportions. Show your work.
A small-market rollout gives you much smaller sample sizes: 20 out of 25 delivered in control, 12 out of 18 in variant. How does your test choice change? Walk through whether you'd use chi-square, Fisher's exact, or a z-test, and either compute the exact p-value or explain precisely how you'd get it.
When does a two-sample t-test produce essentially the same result as a z-test for proportions, and when does it break down? Give at least two concrete failure modes.

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

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit data scientist interview with a pretty gnarly SQL question on subscription churn metrics. The whole thing felt more like a take-home case than a live screen, lots of edge cases baked in and they clearly wanted to see if you'd actually think through the definitions rather than just write a query.

Given monthly end-of-month subscription snapshots, write an ANSI SQL query that computes logo churn count, logo churn rate, gross revenue churn, and net revenue retention for August 2025. Your query must handle users missing from one month via full outer join logic, deduplicate multiple snapshots per user per date using a load timestamp, and treat null MRR as zero.
Using the provided sample data, what are the exact numeric outputs your SQL query would return for logo churn count, logo churn rate, gross revenue churn, and net revenue retention?
How would your churn measurement logic change if you were working with transactional cancellation events instead of monthly snapshots, and what considerations around grace periods and partial-month proration would you need to address?

“This is where I spent most of my time and still felt shaky at the end.”

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit data scientist interview with a meaty ML evaluation question. The whole thing was one technical problem but it had enough layers to keep you busy for a while.

Given a dataset of binary classification outputs with actual labels, predicted labels, and a confidence score per record, write code to compute TP, FP, TN, and FN, then calculate precision, recall, and F1. Also explain how you'd derive predicted labels from confidence scores using a threshold, how that affects the metrics, and how you'd handle division-by-zero edge cases.

“This looked manageable at first and then the threshold part tripped me up a bit.”

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit data scientist technical screen, one question, all SQL. The problem was a cohort retention query and it went deeper than I expected for a phone screen.

Given a users table with signup timestamps and a user_events table with event timestamps, write SQL to compute monthly cohort-based retention. Return cohort_month, month_number (0, 1, 2, ...), cohort size, retained users, and retention rate. A user is retained in month N if they have at least one event in the calendar month that is N months after their signup cohort month. Ignore events that occur before the user's signup timestamp.

“The month 0 case tripped me up first.”

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit DS interview focused entirely on a single product scenario: a pricing page redesign that introduces an annual subscription option. Four connected questions building from impact sizing through experiment design to quasi-experimental fallbacks. Pretty thorough for one case.

Before running any experiment, how would you estimate the business impact (positive and negative) of adding an annual pricing tier to a page that previously only offered monthly plans?
Design a metric framework for this launch: what are your primary success metrics, diagnostic metrics to explain movement, and guardrail metrics to catch unintended harm?
Walk through how you'd design the A/B test for this change: randomization unit, key segments, sample size and power calculations, duration, and how you'd handle pitfalls like SRM or novelty effects.
If an A/B test isn't possible due to engineering or policy constraints and the feature launches to all users at once, how would you still measure impact credibly?

“I started with the obvious upside: annual plans lock in revenue and reduce churn.”

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Intuit·Data Scientist·Technical Phone Screen

Jun 2026

Intuit data scientist interview focused heavily on feature engineering with geographic data. The questions were pretty technical and product-adjacent, which felt like an unusual combo but made sense given the context. No fluff, just a deep dive into one messy, realistic scenario.

What features would you derive from address fields or ZIP codes for a predictive model, and what external public datasets might you join in to enrich those features geographically?
How would you encode ZIP codes and other high-cardinality geographic fields for use in a model?
How do you handle records where ZIP code is missing? Walk through when you'd drop rows, when you'd impute, and whether the missingness itself carries signal.
What risks do you watch for when using geographic and demographic variables in a model, things like fairness concerns, privacy, data leakage, overfitting, or feature staleness?
How would you measure whether adding these geographic features actually improves the model?

“This was the opener and I actually liked it.”

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Intuit·Data Scientist·Technical Phone Screen

May 2026

Intuit data scientist interview with a pretty involved SQL question on cohort retention. Nothing behavioral, just dropped straight into the technical problem and expected a working query.

Given a users table and a user_activity table, write a SQL query to compute cohort-based monthly retention for months 0 through 6. Return the cohort month, months since signup, cohort size, retained users, and retention rate as a decimal.

“This one took me a minute to even set up mentally.”

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Intuit·Data Scientist·Technical Phone Screen

May 2026

Intuit DS interview focused entirely on applied analytics, three connected problems around a TurboTax metric drop, a promo evaluation, and an A/B test design. Heavy on causal reasoning and experiment design, lighter on coding. Felt more like a product analytics case study than a traditional DS screen.

A TurboTax dashboard shows start-to-file conversion dropped from roughly 18% to 12% overnight with no obvious change in traffic or spend. How do you investigate this?
After localizing the KPI drop, what are the most likely root causes and what specific data would you pull to confirm each one?
You have user-level data from a past promotion offering a discount to some users. How do you determine whether the promotion actually drove incremental filing behavior versus just rewarding people who would have filed anyway?
What primary metrics, diagnostic metrics, and guardrails would you use to evaluate whether the promotion was successful?
Design an A/B test for a new onboarding flow meant to increase filing completion. Walk through randomization unit, primary metric, power calculations, and your analysis plan.
If the primary metric improves in your A/B test but a guardrail metric gets worse, what do you recommend?

“My first instinct was to jump straight to user behavior explanations, which was wrong.”

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