Citi Interview Questions — Page 2

Citi·Data Scientist·Technical Phone Screen

Jun 2026

This was a case-style interview for a data scientist role at Citi, basically a product analytics deep dive on a crypto trading scenario. The structure was pretty demanding and they clearly wanted you to think out loud through measurement, segmentation, and recommendations rather than just listing ideas.

BTC market volatility is elevated but retail trading volume on the platform has dropped. Walk through how you'd investigate this with data and translate your findings into a concrete recommendation.
If session counts are stable but executed trade volume is falling, what does that tell you and what would you do next?
How would you test whether a change in risk policy caused the trading volume drop?
During a volatility spike, what metrics would signal that a reliability or infrastructure issue is causing the volume drop rather than a demand issue?
If the volume decline is isolated entirely to new users, how does your recommendation change?

“This is the kind of question where you can really embarrass yourself by jumping straight to 'users are scared of volatility' without checking whether the metric is even real.”

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Citi·Software Engineer·Technical Phone Screen

Jun 2026

Citi technical phone screen for a software engineer role, pretty deep on graph algorithms. One question, but they really wanted the full picture, not just pseudocode.

Design and implement bidirectional Dijkstra to find the shortest path between a source and target node in a weighted directed or undirected graph. Cover the data structures for both search directions, when to stop, how to compute the correct shortest distance when the two frontiers meet, how to reconstruct the path, time and space complexity compared to standard Dijkstra, and edge cases like disconnected graphs or ties.

“This one went longer than I expected.”

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Citi·Data Scientist·Technical Phone Screen

Jun 2026

Interviewed for a Data Scientist role at Citi, heavy SQL focus around crypto trading metrics. Two tasks back to back, both required getting the join logic and date math exactly right under pressure.

Given a trades table and a users table, compute the share of active traders in the last 30 days who made at least 3 trades in that same window. Return the count of active traders, count of power traders, and the ratio as a decimal.
For each signup cohort, compute 7-day retention defined as a user making at least one trade on exactly the 7th calendar day after their signup date. Include users who never traded.

“I got the denominator wrong at first.”

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Citi·Software Engineer·Technical Phone Screen

Jun 2026

Citi systems fundamentals round, basically a quickfire gauntlet covering OS concepts, networking, and browser internals back to back. No real breathing room between topics, which made it harder than any single question would have been on its own.

What is the difference between a process and a thread, and what overhead is involved when the CPU switches between them?
What is the difference between concurrency and parallelism, and can you describe a common concurrency bug and how you'd prevent it?
Compare TCP and UDP. When would you choose one over the other, and how do flow control and congestion control work in TCP?
Walk through everything that happens from typing a URL into a browser to the page being fully rendered on screen.

“Knew this one cold.”

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Citi·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026

Behavioral prep material for a remote Data Scientist role at Citi, focused on three core prompts around data influence, data quality, and cross-functional prioritization. No specific outcome or date is mentioned, reads more like structured prep than a post-interview debrief.

Tell me about a time your analysis changed a decision that was already heading in a different direction. What did you look at, and how did you bring stakeholders along?
Describe a situation where you were working with data that turned out to be unreliable or missing. How did you catch it, and what did you put in place so it wouldn't happen again?
When you get competing requests from product, engineering, legal, and risk teams with no clear owner and everyone thinks their thing is urgent, how do you decide what to work on first?

“This one is trickier than it sounds because you have to walk a line between 'my analysis was right' and 'but the data was observational so I can't be certain.' I kept wanting to just say the model proved it, but the interviewer would probably push back on causality.”

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Citi·Data Scientist·Technical Phone Screen

Jun 2026

Interviewed for a Data Scientist role at Citibank and got a pretty deep dive into credit loss accounting concepts, specifically around CECL. Not what I expected going in, but it made sense given the domain.

How does the CECL framework differ from the older incurred loss model, and what are the practical implications around lifetime loss estimation, forward-looking forecasts, loan pooling, and the resulting effects on allowance levels and earnings volatility?

“This was a lot to unpack in one question.”

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Citi·Software Engineer·Technical Phone Screen

Jul 2026

Citi software engineer interview that leaned pretty heavily on CS fundamentals. The questions were textbook but they pushed for specifics on complexity and tradeoffs, not just surface-level definitions.

Walk me through how a hash map works under the hood, including how collisions are handled, what load factor means, and how resizing works. What are the average and worst-case time complexities for search, insert, and delete?
What are the tradeoffs between arrays and linked lists, specifically around random access and insertion performance?
When would you use a stack versus a queue? What are the time complexities for their core operations?

“This is the question I thought I had cold and then fumbled the worst-case part.”

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Citi·Data Scientist·Technical Phone Screen

Jul 2026

Citibank data scientist interview with a pretty involved Excel-based case question around credit risk visualization. One question, technical, and it required more domain fluency than I expected going in.

Using Excel, walk through how you would build visuals to communicate portfolio expected loss, risk-weighted assets, and concentration risk. What specific PivotTables, charts, or heatmaps would you create, what fields and measures would you use, and why do those choices actually help decision-makers?

“I knew the EL and RWA concepts well enough but the 'why does this help decision-making' part is where I got a bit handwavy.”

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Citi·Data Scientist·Technical Phone Screen

Jul 2026

Citibank data scientist interview that went pretty deep into probability and expected value territory. The main question was a multi-part die-rolling problem that escalated from a clean derivation into generalization and then a break-even cost analysis. Not what I was expecting from a bank role but here we are.

You roll a fair six-sided die repeatedly, collecting the face value each roll. You must keep rolling if you get 4, 5, or 6, and stop on 1, 2, or 3. Derive the expected total payout, showing your work via conditioning on the stopping time or an infinite series.
Generalize the die problem: suppose P(forced to roll again) = p and the expected value of a single roll is μ. Express the expected total payout in terms of p and μ.
Now suppose every roll after the first costs a fixed fee c. What value of c makes the game fair (expected net payout = 0)?

“This part I actually handled okay.”

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Citi·Data Scientist·Technical Phone Screen

Jul 2026

Citibank data scientist interview that went deep into model risk territory. The whole thing revolved around one big technical question on PD model validation, and they clearly expected you to know the full lifecycle, not just throw out buzzwords.

Walk me through how you would validate a newly developed probability of default model, covering data partitioning, discrimination metrics, calibration, stability monitoring, backtesting, challenger models, and documentation.

“This is a lot to cover in one question and I kind of panicked trying to sequence it all.”

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