Intuit Interview Questions — Page 4

Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit product analyst interview with a meaty experimentation question built around their onboarding flow. One question, but it had a lot of layers and I definitely underestimated how much structure they expected.

The product team added a progress bar to a 4-step onboarding flow (onboard, send payment, set up bank account, file taxes). How would you evaluate whether it's actually helping? Walk through your hypothesis, the metrics you'd track, how you'd design the experiment, and any pitfalls you'd watch out for.

“I started okay with the hypothesis part, said something like 'helps means more users completing all four steps without dropping off midway.' But then I got a bit tangled on which metric to call primary vs diagnostic.”

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Intuit·Software Engineer·Technical Phone Screen

May 2026

Interviewed for a software engineering role at Intuit and got hit with a two-part session covering a classic DP problem and a React component build. The algorithm half felt pretty standard but the React part had some specific constraints around performance that tripped me up a bit.

Given a string and a dictionary of valid words, write an algorithm to determine if the string can be broken into a sequence of dictionary words. Return one valid segmentation if possible, then explain how you'd extend it to return all valid segmentations. Also walk through time/space complexity and compare a DP approach versus BFS, including any optimizations like a trie.
Build a React component that checks in real time whether a user's text input is a palindrome, ignoring case and non-alphanumeric characters. The component should avoid unnecessary re-renders, handle long inputs efficiently using debouncing and a two-pointer approach, and include unit tests for the core normalization and validation logic.

“I started with the DP approach because it felt more natural to me and I could talk through memoization pretty confidently.”

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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·Software Engineer·Technical Phone Screen

May 2026

SQL interview at Intuit for a software engineer role. One meaty query question that looked straightforward at first but had a lot of moving parts once you got into the normalization and aggregation logic.

Given a transactions table with columns for transaction ID, status, amount, and a messy nullable reason field, write a SQL query that returns one row per status showing the total transaction count, total amount, and a concatenated string of the most common normalized failure reasons ordered by frequency.

“This one took me longer than I expected to get right.”

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Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit SQL round, one meaty question about parsing currency strings stored as VARCHAR. Not a vibe check, just pure technical execution under pressure.

A database column stores monetary values as strings with suffixes like '1.5M', '2B', or '750K'. Write a SQL query to convert these into actual numeric values (K = 1,000, M = 1,000,000, B = 1,000,000,000) and then aggregate, sort, or filter on the result.

“This one took me longer to get rolling than I expected.”

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Intuit·Machine Learning Engineer·Technical Phone Screen

May 2026

Interviewed for an MLE role at Intuit and a chunk of the technical portion zeroed in on feature stores specifically, which I wasn't fully expecting. Not a bad experience, just very infrastructure-heavy.

Have you worked with feature stores before? Walk me through that experience.
What feature store technologies have you used or evaluated?
What are the core responsibilities of a feature store in an ML system?
How do you make sure features computed offline match what gets served online at inference time?

“I had some experience but nothing deep, so I described a project where we were managing features manually and talked about the pain points that led us to look at feature store solutions.”

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Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit technical phone screen, bash scripting focus. The question sounds like a warmup until they start asking about edge cases and you realize you should've practiced more than just the happy path.

Given a directory, write a bash script to find and return the largest file by size. Walk through multiple approaches, handle filenames with spaces or newlines, and explain how you'd avoid pitfalls when parsing ls output.

“Started with the ls -l approach because it felt natural, and the interviewer let me go down that road for a bit before asking what happens with filenames that have spaces.”

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Intuit·Software Engineer·Technical Phone Screen

May 2026

Intuit technical phone screen centered on tree reconstruction from a parent array, with follow-up queries layered on top. Pretty standard stuff for a mid-level SWE role but the breadth of follow-ups they could ask kept me on edge the whole time.

Given a parent array representing a rooted tree, reconstruct the tree structure and answer queries on it such as node depth, subtree sizes, children ordering, lowest common ancestor, or root-to-leaf path aggregations.

“This one sprawled way more than I expected.”

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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·Software Engineer·Technical Phone Screen

Jun 2026

Intuit product analyst interview with a meaty ML scenario around a tax-filing prediction model. The whole thing revolved around one feature and the questions kept pulling at different threads of the same problem, which I actually liked more than I expected.

A key feature in your model has a lot of zeros and missing values. How do you figure out why, and how do you handle each case differently during modeling?
If a feature isn't available at inference time but exists in training data, what are your options and how do you choose between dropping it, building a proxy, or reframing when the prediction happens?
The feature is negatively correlated with success. Give at least two plausible explanations including a contrasting interpretation, and describe how you'd validate or rule out each one.

“This is where I spent the most time and probably rambled a bit.”

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