Squarepoint Interview Questions — Page 2

Squarepoint·Software Engineer·Technical Phone Screen

Jun 2026

ML system design round at Squarepoint for a quant researcher role, focused on credit card transaction data. The first part was mostly about data cleaning basics, which felt a bit anticlimactic given the 'finance intuition' billing.

You're given a credit card spending time series with timestamp, customer ID, and merchant fields. How would you handle missing values in this dataset?

“Expected something more finance-flavored given how the question was framed beforehand, so the NA handling opener felt weirdly basic.”

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Squarepoint·Software Engineer·Take-home Assignment

Jun 2026

Squarepoint Capital had me build two React components from scratch and then pick them apart myself. The rating widget was more involved than I expected, and the Todo app follow-ups turned into a pretty wide-ranging design conversation.

Build a reusable React rating component that supports configurable icon count, half-step selection, mouse/touch/keyboard interactions, accessibility with ARIA, and both controlled and uncontrolled modes.
Build a React Todo application with add/edit/delete/toggle, filtering, localStorage persistence, and basic tests.
How would you improve the Todo app's performance, and what techniques would you use?
How would you make the Todo app more accessible, including keyboard navigation and screen reader support?
What UX improvements would you add to the Todo app, such as inline editing, due dates, or drag-and-drop reordering?
How would you scale the Todo app to sync with a backend API, including handling optimistic updates?
What would your testing strategy look like for these components, and how would you think about TypeScript adoption and error boundaries?

“The ARIA and keyboard parts took longer than I budgeted.”

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Squarepoint·Data Scientist·Technical Phone Screen

Jun 2026

Squarepoint Data Scientist interview with a greedy/algorithmic problem that looks deceptively simple but has some real edge cases to think through. One question, coding focused, no behavioral stuff from what I can tell.

A store has n products each with some stock quantity. Customers buy one unit at a time, and the price paid equals the current stock level before the sale (so stock of 5 means the customer pays 5, then stock drops to 4). Given m customers, figure out how to assign sales to maximize total revenue. What's an efficient algorithm for large inputs?

“The greedy insight is pretty clean once you see it: always sell from the product with the highest current stock, because that gives you the biggest price right now.”

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Squarepoint·Software Engineer·Technical Phone Screen

Jul 2026

Second part of a Squarepoint quant researcher loop, focused on what you can actually infer from transaction data at different levels of granularity.

Given a dataset of transaction records, what kinds of insights or signals could you extract from it, ranging from individual merchant or stock level (e.g. Apple sales) up to sector-wide or macroeconomic trends?

“This was more open-ended than I expected.”

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Squarepoint·Data Scientist·Technical Phone Screen

Jul 2026

Squarepoint Data Scientist interview had a pretty math-heavy quantitative finance angle. The main question was about Sharpe ratios and what happens when you filter out zero-return observations. Not the usual ML or SQL stuff I'd prepped for.

You have a return series with 2n observations, exactly half of which are zero. Compare the Sharpe ratio computed on all 2n observations versus the Sharpe ratio computed only on the n non-zero observations. Is the filtered Sharpe higher or lower, and does it depend on the sign of the mean return? Also, does the conclusion change if the n removed observations aren't necessarily the zero ones?

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

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Squarepoint·Data Scientist·Technical Phone Screen

Jul 2026

Squarepoint Capital data scientist interview with a coding focus. The question was finance-flavored but really just a dynamic programming problem dressed up in quant clothing.

Implement a function mdd(pnl) that takes a cumulative PnL series and returns the maximum drawdown as a negative value, along with the start and end indices of that drawdown period.

“Looks like a finance question but it's basically the buy-and-sell stock problem with extra steps.”

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Squarepoint·Software Engineer·Technical Phone Screen

Jul 2026

Technical screen at Squarepoint for a software engineer role. The main problem was a payroll file parser built entirely with the Python standard library, which sounds manageable until you realize they want generalized schema handling, type coercion, dedup logic, and three query functions all tied together cleanly.

You're given a payroll file as a StringIO object with a header row and manually entered data. Using only the Python standard library, parse it into an in-memory structure that handles missing fields, extra whitespace, inconsistent delimiters, empty lines, and duplicate IDs. The parser should be driven by the header metadata so it works for files with different column sets.
After parsing, write a function that counts how many employees have a salary strictly above 30,000. Handle missing or invalid salary values without crashing.
Write a function that returns the employee with the longest job tenure based on a start date column. Define and document how you handle rows with missing or unparseable dates, and handle ties explicitly.
Write a function that returns the employee with the second-highest salary. Define clearly how you handle duplicate salary values at the top.

“This was the core of the whole problem and I underestimated how much design thinking they wanted up front.”

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