Upstart Interview Questions — Page 3

Upstart·Software Engineer·Technical Phone Screen

Apr 2026

Got a coding question at Upstart for a software engineer role that was deceptively tricky once you got into the edge cases.

Write a parser for a comma-separated string that handles quoted segments, where quotes preserve internal commas and other characters. For example, the input `Hello,'Hi "to" you'` should produce `['Hello', 'Hi "to" you']`.

“Looked easy at first glance and then I started thinking about nested quotes and my brain kind of froze.”

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Upstart·Software Engineer·Technical Phone Screen

May 2026

Upstart SWE interview with a geometry/coordinate problem. Pretty focused on getting the math right and handling the data structure cleanly.

Given a list of 2D coordinate pairs, compute the bounding box: find the minimum X, minimum Y, the width (maxX minus minX), and the height (maxY minus minY).

“Straightforward once you see it but I tripped over the iteration for a second.”

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Upstart·Data Scientist·Technical Phone Screen

May 2026

Interviewed for a Data Scientist role at Upstart. The question was a meaty causal inference design problem centered on ride-share driver supply, and it pushed pretty hard on experimental design fundamentals. Not a warm-up round.

Design an experiment to measure whether surge-pricing push notifications sent to drivers actually increase supply at the airport. What primary and secondary metrics would you track, and how would you handle spillover effects onto drivers who weren't part of the treatment group?

“This one took me a minute to get my footing.”

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Upstart·Data Scientist·Onsite - Behavioral / Leadership

May 2026

Onsite behavioral round at Upstart for a Data Science role, focused heavily on fair lending concepts and some standard personal questions mixed in. The fairness question was the real meat of the session and it went deeper than I expected.

A lender issues 50% of loans to women and 50% to men. Does that split alone guarantee fair lending, and what additional analyses would you run?
Why are you interested in machine learning?
Tell me about a past failure and what you would do differently.

“I started with the obvious point that raw issuance counts tell you almost nothing without knowing the underlying applicant pool.”

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Upstart·Software Engineer·Technical Phone Screen

May 2026

Upstart software engineer screen, just one coding question. Pretty straightforward stuff, nothing that'll keep you up at night.

Given an integer array, remove all odd numbers and return the remaining even numbers in reverse order.

“Easier than I expected, honestly braced for something harder.”

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Upstart·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at Upstart, one big question about building a user registration system from scratch. Covers a lot of ground so be ready to jump between security, scaling, and product concerns in the same breath.

Design a user signup and registration system. Walk through the API design, data model, input validation, password storage, email/phone verification, anti-abuse measures, integration with auth and downstream services, data store choices, and how you'd handle scale and regional availability. Also discuss the failure modes you'd defend against.

“This question is basically a gauntlet.”

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Upstart·Data Scientist·Technical Phone Screen

May 2026

Upstart data science interview that went deep on marketing analytics. One big multi-part case covering everything from funnel math to incrementality testing. It was a lot to hold in your head at once and I don't think I nailed all of it.

Given a marketing dataset with daily channel-level data (spend, visits, form starts, form completions, loans funded, revenue), build a conversion funnel showing stage-to-stage rates and customer acquisition cost at each meaningful milestone.
Compute ROAS and payback period under both last-click and 7-day first-touch attribution. If you only had raw click and impression logs, how would you re-attribute conversions?
Using a simple response curve like a log or Hill function, recommend how to shift next month's budget across channels and estimate marginal ROAS for each.
Design an incrementality test (geo-based or time-based holdout) to validate your budget recommendations. Cover unit of randomization, sample size, contamination risks, and success criteria.
Which channels might look effective in attribution reports but are actually non-incremental due to retargeting or brand spillovers? How would you diagnose this?

“Felt pretty comfortable here.”

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Upstart·Data Scientist·Technical Phone Screen

May 2026

Upstart data scientist interview was pretty much a gauntlet of probability and stats questions. Four problems back to back, ranging from radioactive decay to Monty Hall. Felt like a grad school exam more than a job interview.

A radioactive atom has a half-life of 1 day and decays following an exponential distribution. Starting with 100 atoms, what is the probability distribution of the number still alive after 10 days? Also compute the expected count and the probability at least one survives, then write a simulation that outputs each atom's final state.
Given y = x + epsilon where x and epsilon are independent standard normals, compute the population OLS slope and intercept for regressing y on x, and then separately for regressing x on y.
In the classic 3-door Monty Hall setup, what are your winning probabilities if you switch versus if you stay? Justify briefly.
You roll a fair n-sided die repeatedly. What is the expected number of rolls needed to see every face at least once?

“This one took me a second to set up properly.”

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Upstart·Data Scientist·Onsite - Behavioral / Leadership

May 2026

Upstart data scientist interview with a single but very loaded behavioral question that basically asks you to walk through an entire analytics project lifecycle, including what happens when you were wrong. It's the kind of question that sounds manageable until you realize they want specific dates and metrics.

Walk me through a time you tackled an ambiguous analytics problem with incomplete data under deadline pressure. How did you frame the problem and define success upfront? What options did you weigh and how did you make a call despite uncertainty? How did you handle stakeholder disagreement and which trade-offs did you consciously pass on? And then: six weeks later you find out you were wrong. What do you do, how do you tell people, and how do you keep it from happening again?

“This question is four questions stapled together and they want all of it.”

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Upstart·Data Scientist·Onsite - Multi Round

May 2026

Onsite stats and probability round for a research-scientist-adjacent data science role at Upstart. Two questions, both pretty theoretical, and the second one had a Bayesian twist I wasn't fully expecting in what felt like a standard frequentist setup.

Given the data-generating process y = X + ε where X and ε are independent standard normals, what happens if you flip it and regress X on y using OLS? Derive the regression coefficient.
A sample of 100 children from a village shows 50 from 1-child families, 30 from 2-child families, and 20 from 3-child families. Estimate the proportion of 1-child families, build a 95% confidence interval for it, and then explain how you'd get an exact credible interval using a Dirichlet prior on the family-size proportions.

“This one got me for a second because my instinct was to just say 0.5 and move on, but they wanted the full derivation.”

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