Citadel Interview Questions — Page 12

Citadel·Software Engineer·Technical Phone Screen

Jun 2026

Citadel quant researcher interview with a pretty gnarly probability/statistics question that required you to actually derive an answer from scratch rather than just pattern-match to something you'd seen before. The expected answer was around 16 days, which at least gave me something to sanity-check against.

You run an LLM-based sentiment analysis pipeline and each day it produces a vector of scores over the same dataset. The pairwise correlation between any two days' outputs is about 0.95. If you average together n days of outputs into a single vector, how many days n do you need so that two independently-averaged n-day windows have a correlation above 0.99 with each other?

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

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Citadel·Data Scientist·Technical Phone Screen

Jun 2026

Citadel Data Scientist interview with a tight algorithms question that pushed you to think beyond the obvious sorting approaches. The problem had a clever constraint that basically hands you the answer if you know your data structures.

You're given an array of n elements where each element is at most k positions away from where it would be in the sorted array. Design an algorithm to sort it more efficiently than O(n log n) and explain the time and space complexity.

“The k constraint is basically screaming 'use a heap' but I spent an embarrassing amount of time second-guessing myself before committing to that.”

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Citadel·Software Engineer·Technical Phone Screen

Jun 2026

Citadel SWE interview that went deep on range minimum queries, covering like four different implementations and their trade-offs. They also threw in a sliding window variant at the end. Pretty dense technically and they wanted actual code for the segment tree.

Given an array of integers, implement range minimum queries supporting rmq(l, r). Walk through multiple approaches and their trade-offs, including segment tree, sparse table, block decomposition, and the O(1) Cartesian tree approach.
Code the segment tree implementation for range minimum queries, including build and query.
Now consider a sliding window minimum problem where the window size is fixed at k. How would you solve it efficiently?

“This one took a while.”

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Citadel·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Behavioral portion of a later round at Citadel for a Data Scientist role. Pretty standard PhD background question but it carries more weight than it looks like it will.

Walk me through the graduate-level coursework and research you did during your PhD. What accomplishment from that time are you most proud of, and what makes it stand out to you?

“I rambled a bit trying to cover too much ground.”

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Citadel·Data Scientist·Technical Phone Screen

Jun 2026

Citadel data science interview focused heavily on wind power prediction modeling, which I was not expecting to be so physics-adjacent. One long technical question that branched into a dozen sub-topics.

How would you design a regression model to predict wind turbine power output from a single snapshot of weather and turbine features, without using time-series modeling?
How would you handle missing sensor readings and noisy or outlier data in this wind power prediction setting?
Walk through your model selection reasoning for this problem. What would you consider between regularized linear models, gradient boosting, random forests, and a shallow neural network?
How would you validate this model across different wind farm sites and varying wind speed conditions to make sure it generalizes?
What evaluation metrics would you use, and how would you handle the fact that errors are not constant across the power range?
How would you estimate and calibrate prediction uncertainty for this model, and what safeguards would you put in place for extrapolation or curtailment scenarios?

“This was the whole interview basically.”

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Citadel·Software Engineer·Technical Phone Screen

Jun 2026

Citadel quant engineer interview that went deep on ML fundamentals, specifically a big compare-and-contrast question across four model families. More conceptual than I expected for a quant role, felt like they were testing breadth as much as depth.

Compare and contrast reinforcement learning, CNNs, Transformers, and linear regression across four dimensions: the problem setting each is designed for, the core architectural idea or inductive bias, how training works, and representative use cases. Then explain which family fits best for tabular regression, image classification, sequence modeling, and sequential decision-making, and why.

“This was basically a mini oral exam.”

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Citadel·Data Scientist·Technical Phone Screen

Jun 2026

Citadel data science interview that went deep into stochastic processes and trading strategy math. Four-part problem covering random walks, profit distributions, and Brownian motion limits. Felt like a quant interview wearing a data science costume.

For a simple symmetric random walk, consider a strategy where you open a long or short position at each step based on the direction of that step, hold all positions until the final time T, and liquidate at the terminal price. Derive the expected total profit and its variance.
Now modify the strategy so that each position is closed after just one step instead of held until the end. What are the expectation and variance of a single one-step profit, and what happens to the cumulative profit after T steps?
Generalize the original hold-to-end strategy to a biased random walk where the probability of an up step is p, not necessarily one-half. How do the expected profit and variance change?
Replace the discrete random walk with standard Brownian motion sampled at equally spaced intervals. Apply the same trading rule using the sampled increments, then discuss what happens to the expected profit and variance as the sampling interval shrinks to zero. Are there additional assumptions needed, and does the limit exist?

“The expectation part wasn't bad once I wrote out what each position contributes: a long at time t earns S_T minus S_t, a short earns the negative.”

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Citadel·Data Scientist·Onsite - System Design / Architecture

Jun 2026

Citadel data scientist interview with a single meaty system design question covering the full ML pipeline for real estate price prediction. The depth they expected was pretty intense, covering everything from feature engineering to fairness audits to deployment monitoring.

Design a complete machine learning system to predict residential property sale prices across a large city, covering feature selection and engineering, training and validation strategy, model choice, fairness considerations, and deployment and monitoring.

“This was a beast of a question and I spent the first two minutes just trying to figure out where to start.”

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Citadel·Software Engineer·Technical Phone Screen

Jun 2026

Did a coding round for a software engineer role at Citadel. Just one question but it had some room to go in different directions depending on how you read the problem.

Design a tree node data structure and write a function to compute the sum of all values in the tree.

“The problem didn't say binary tree, which I noticed pretty quickly and went with an N-ary structure where each node holds a list of children.”

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Citadel·Data Scientist·Technical Phone Screen

Jun 2026

Citadel Data Scientist interview with a pretty heavy quant optimization problem. One question, no fluff, straight into the math.

Given an unconstrained quadratic optimization problem min f(x) = x^T Q x + c^T x over real-valued vectors, where Q is not necessarily symmetric, under what conditions does a finite minimum exist? If it does, characterize the set of minimizers and the optimal value.

“This one took me a second to settle into because Q being non-symmetric felt like a trick at first.”

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