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Two Sigma interview notes, page 2 of 7

Large Enterprises

Most of these Two Sigma write-ups come from Data Scientists (31), then Software Engineers (30) and Machine Learning Engineers (1). The most reported rounds are the technical phone screen (53), the onsite coding round (5), the online assessment (2) and the hiring manager screen (1).

62 interview notes · updated Jul 2026

Two Sigma·Data Scientist·Technical Phone Screen

Apr 2026
Two Sigma data scientist interview, pretty heavy on applied ML and statistics. The whole thing felt like a coding exercise dressed up as a data problem, which I wasn't fully expecting. Walked away thinking I should have reviewed streaming algorithms more carefully.
  • Given daily temperature data for NYC and several nearby towns, which town has the largest temperature fluctuation over time? You need to define the metric you use.
  • Which town's temperature pattern is most similar to NYC's? Define the similarity metric you use.
  • Use the towns' temperature data to predict NYC temperature with a regression model. Evaluate it using MSE.
  • Implement greedy forward feature selection: given a target number k, iteratively add the town that most reduces validation MSE until k towns are selected. Return the selected towns and final MSE.
  • Implement simple linear regression without an intercept for a single predictor. First solve the batch case, then solve the streaming case where data arrives one pair at a time and the slope estimate updates incrementally.

“The 'define your metric' part is where I stumbled.”

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Two Sigma·Software Engineer·Technical Phone Screen

Apr 2026
Two Sigma Research Engineer interview with a coding round that leaned heavily on applied math and stats rather than pure algorithms. The linear regression question had two parts and the second one tripped me up more than I expected.
  • Implement linear regression through the origin (no intercept) from scratch. First, solve it in the standard batch setting where you have the full dataset available. Then extend it to a streaming setting where data arrives one point at a time and you need to update your estimate incrementally without reprocessing everything.

“The batch part was fine, just derive the closed-form slope estimator and compute it directly.”

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Two Sigma·Software Engineer·Technical Phone Screen

May 2026
Two Sigma SWE interview with a graph/pathfinding problem that looks deceptively simple until you realize it's a max-product path problem dressed up as a currency exchange puzzle. One question, but it had enough layers to keep me busy for the whole session.
  • Given a set of currencies and directed exchange rates between pairs, find the maximum amount of a target currency you can obtain from 1 unit of a starting currency by chaining any sequence of exchanges (no repeated currencies in a path). Return -1 if the target is unreachable.

“My first instinct was Dijkstra, which was wrong because you want to maximize a product, not minimize a sum.”

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Two Sigma·Software Engineer·Onsite - Coding / Algorithms

May 2026
Two Sigma coding round for a software engineer role. One question, but it had layers that I didn't fully appreciate until I was already halfway through a naive solution.
  • Implement a hash map from scratch supporting put and update, and get operations, using only arrays and your own linked list nodes. No built-in hash table types allowed. Must handle collisions via separate chaining and resize dynamically when the load factor exceeds 0.75.

“I got the basic structure down pretty fast, bucket array plus linked list nodes per bucket, and the put and get logic wasn't too bad.”

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Two Sigma·Software Engineer·Technical Phone Screen

May 2026
Two Sigma software engineer interview with a grid-based traversal problem. The problem had enough edge cases to keep things interesting, and the DFS angle felt pretty standard for this type of company.
  • Given a grid representing a sewer or drain layout, determine whether water can flow from a source cell to a destination cell through connected pipes. Use DFS to traverse the grid, handle visited cells to prevent cycles, and explain how adjacency or connectivity is defined between cells.

“The connectivity definition part is what tripped me up a bit.”

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Two Sigma·Data Scientist·Technical Phone Screen

May 2026
Two Sigma data science interview with a statistics-heavy question about t-statistics. One question, pretty deep, felt like they wanted to see if you actually understood what the math was doing rather than just reciting a formula.
  • You have an estimated coefficient from a regression or A/B test and its standard error. Why is the t-statistic a useful summary of evidence? What does it capture that the raw estimate alone doesn't, how does it connect to p-values and confidence intervals, and when can it be misleading?

“I started with the intuitive angle: a coefficient of 0.5 means nothing if you don't know how noisy the estimate is.”

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Two Sigma·Data Scientist·Onsite - Coding / Algorithms

May 2026
Two Sigma coding round for a Data Scientist role. Two questions, both algorithmic, and the second one was a lot more involved than I expected going in.
  • Implement merge sort on an array of integers, return the sorted array, and walk through the time and space complexity.
  • Given a binary matrix of 0s and 1s, find the area of the largest rectangle made entirely of 1s.

“Felt pretty solid here.”

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Two Sigma·Software Engineer·Technical Phone Screen

May 2026
Two Sigma Quant Engineer interview that was essentially one long end-to-end data science case study built around predicting bike rental prices. The whole thing was a pipeline walkthrough and they pushed hard on the reasoning behind every decision, not just the mechanics.
  • Given raw Citibike or rental data, which features would you use to predict price, and why?
  • How would you handle missing data, outliers, and anomalies like COVID-era disruptions in your feature engineering?
  • Compare linear regression and random forest for this prediction task. What assumptions does each make, and what are the tradeoffs?
  • How would you handle seasonality in a linear regression model for this problem?
  • How would you split your data for validation, and would you use time-aware or random splits?
  • Which evaluation metric would you choose for this regression problem and why? Walk through when you'd use MSE, MAE, or other metrics.

“This is where I spent too long listing obvious stuff like time of day and weather without really justifying the causal logic.”

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Two Sigma·Data Scientist·Technical Phone Screen

May 2026
Two Sigma data scientist interview with a meaty open-ended question about duplicate detection in transaction data. No fluff, they just dropped a scenario and let you run with it. Felt more like a whiteboard design session than a typical DS screen.
  • You have a dataset of credit card transactions and suspect some records are duplicates. How would you define what counts as a true duplicate, identify real-world causes of duplication, and then detect, quantify, and extract both exact and near-duplicate records in a robust way?

“This one sprawled in a way I wasn't ready for.”

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Two Sigma·Software Engineer·Onsite - Coding / Algorithms

May 2026
Two Sigma coding round, one problem the whole session. Tree structure, independent set, O(n) required. Felt manageable but the edge case stuff they asked me to design myself was where I started second-guessing.
  • Given a tree of n nodes, find the maximum independent set: the largest subset of nodes such that no two selected nodes share a direct edge. Your solution must run in O(n) time, and you need to design your own edge case tests covering things like a single node, a path graph, and a star graph.

“Classic tree DP once you recognize what it is, but I blanked for a bit on the recurrence.”

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