← Back to Directory

Citi

Large Enterprises

Most of these Citi write-ups come from Data Scientists (17), then Software Engineers (6). The most reported rounds are the technical phone screen (21) and the behavioral / leadership round (2).

23 interview notes · updated Sep 2026

Citi·Software Engineer·Technical Phone Screen

Aug 2026
Karat technical screen for a Python role at Citi. Two coding problems, and the second one did not go well at all.
  • Write a Python function that returns a specific data structure based on given inputs (second coding problem in the Karat screen).

“Got the return type right but the actual values were wrong.”

View Post

Citi·Data Scientist·Technical Phone Screen

Jul 2026
Quant-heavy technical screen for a Data Scientist role at Citibank. The whole thing was binomial tree pricing, both equity options and fixed income, which I wasn't expecting to be this deep for a DS position. Left feeling like I'd done okay on the mechanics but probably fumbled the conceptual explanation on the American call question.
  • Given a two-period binomial stock model with S0=100, up factor 1.25, down factor 0.8, and a 5% per-period risk-free rate, compute the risk-neutral probability and price a European call with strike 100 using backward induction.
  • Using the same binomial tree, price an American put with strike 100 and identify any nodes where early exercise is optimal.
  • Prove whether early exercise of an American call on a non-dividend-paying stock can ever be optimal in this binomial framework, and explain the intuition.
  • Using a two-period short-rate tree with given up and down rates and risk-neutral probability of 0.5, price a zero-coupon bond maturing at t=2 with face value 100.
  • Price a two-period coupon bond with face 100 and coupon 5 paid at t=1 and t=2 using the same short-rate tree, then report its yield to maturity and explain the no-arbitrage pricing principle behind your approach.

“The risk-neutral probability calculation itself is fine, just plug into the formula.”

View Post

Citi·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for a Data Scientist role at Citi, focused pretty heavily on risk and product analytics judgment. Three questions, all variations of the classic 'show me you did something real' prompts. Nothing too surprising but the follow-ups had teeth.
  • Tell me about a time your analysis actually changed a decision that was already in motion.
  • Walk me through a situation where you discovered the data you were working with was unreliable. What did you actually do about it?
  • How do you handle it when a stakeholder brings you a request that isn't clearly defined and you're not sure what they actually need?

“This is the one I felt best about.”

View Post

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.”

View Post

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.”

View Post

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.”

View Post

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.”

View Post

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.”

View Post

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.”

View Post

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.”

View Post

Citi interview FAQ

What rounds are in the Citi interview?

Candidates most often report a technical phone screen (21 write-ups) and a behavioral / leadership round (2).

What does the Citi technical phone screen cover?

Across 21 technical phone screen write-ups, the most tagged question topics are Technical Trade-offs (25 questions), Product Analytics & Metrics (18) and Algorithms & Data Structures (13).