Citi Interview Questions — Page 1

Citi·Data Scientist·Technical Phone Screen

Apr 2026

Citibank data scientist interview that went deep into credit risk math pretty fast. One big technical case covering the full EL and RWA pipeline, which sounds manageable until you're actually sitting there trying to remember what to do with off-balance-sheet items under pressure.

Given anonymized loan data with probability of default, loss given default, and exposure at default figures, how would you compute portfolio expected loss and risk-weighted assets? Walk through your assumptions, formulas, aggregation method, how you'd handle off-balance-sheet exposures, and any sensitivity analysis you'd run.

“This is a meaty one.”

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Citi·Data Scientist·Technical Phone Screen

Apr 2026

Citibank data scientist interview that leaned hard into ethics and governance territory, probably because of all the regulatory baggage in the industry right now. Just one question but it had a lot of moving parts.

Given past industry scandals around sales practices, how would you make sure models are developed and deployed ethically? Think about transparency, documentation, bias testing, governance structures, and independent review.

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

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Citi·Data Scientist·Technical Phone Screen

Apr 2026

Interviewed for a Data Scientist role at Citibank and got hit with a pretty heavy risk/portfolio case question. More quant-heavy than I expected for a DS interview, felt like it was testing credit risk intuition as much as anything else.

Given a portfolio, identify the top five risk exposures and propose specific mitigation actions for each. Back up your recommendations with both quantitative and qualitative reasoning.

“This one took me a minute to organize.”

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Citi·Data Scientist·Technical Phone Screen

Apr 2026

Citi data scientist interview that leaned heavily into product experimentation design, specifically around a crypto activation problem framed through a Coinbase scenario. The whole thing felt more like a product analytics case than a traditional DS interview, which I wasn't fully expecting.

Design an A/B test for a product change meant to increase the rate at which new retail users complete their first trade after signing up on a crypto platform.
What clarifying questions would you ask before designing this experiment, and why do they matter?
The experiment shows a positive overall result but one high-risk user segment is trending negative. How do you handle that?
Your result is directional but doesn't reach statistical significance. What do you do with that?
How would you tell apart a genuine activation lift from users just making one low-quality or throwaway trade to satisfy the metric?
What would your monitoring dashboard look like during the ramp-up phase of this experiment?

“This was the main question and it ate up most of the session.”

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Citi·Software Engineer·Technical Phone Screen

Apr 2026

Citi software engineer interview that was basically two classic brain teasers back to back. The kind of session where you know the puzzles exist somewhere in the universe but blanking under pressure is very real.

You have 12 identical-looking balls where exactly one is heavier. Using a balance scale exactly three times, identify the heavier ball and explain why three weighings are both sufficient and necessary.
Four people need to cross a bridge at night with one torch. Their individual crossing times are 1, 2, 7, and 10 minutes. Only two can cross at once and the torch must travel with them. What is the minimum time to get everyone across, what sequence achieves it, and how do you prove it's optimal?

“I knew this puzzle existed but could not reconstruct the full solution cleanly under pressure.”

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Citi·Data Scientist·Technical Phone Screen

May 2026

Citibank data scientist interview that went pretty deep into regression theory and model diagnostics. Not a casual screen, they clearly wanted someone who could reason through the math and not just name-drop techniques.

What are the core OLS assumptions required for estimates to be unbiased, efficient, and consistent? For each one, name a diagnostic test and a remedy if the assumption is violated.
You have n=10,000 observations where the error variance is proportional to x1 squared and two predictors x2 and x3 are correlated at 0.98. Walk through exactly how you'd validate the model, refit it, and compare results including what happens to your standard errors, confidence intervals, and hypothesis tests.
Under what circumstances would you move away from OLS toward ridge regression, LASSO, or a GLM, and what's the reasoning behind each switch?

“I knew most of these but fumbled the normality one.”

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Citi·Software Engineer·Technical Phone Screen

May 2026

Citi software engineer interview that was basically one long resume deep-dive. They wanted everything: context, your specific role, decisions, numbers, trade-offs, and what you'd redo. More structured than I expected for what felt like a casual walkthrough.

Walk me through your resume, and for your most recent project cover: the problem you were solving, what you personally owned, the key technical decisions and your reasoning, concrete measurable outcomes, the hardest trade-off you navigated, and what you'd change if you did it over.

“This is a lot to hold in your head at once and I didn't pace it well.”

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Citi·Data Scientist·Technical Phone Screen

May 2026

Interviewed for a Data Scientist role at Citibank. The technical screen leaned heavily into credit risk modeling fundamentals, specifically around logistic regression and its practical limits in a PD context. Not a lot of fluff, just one meaty question that took the whole conversation.

What are the limitations of logistic regression when used for probability of default modeling? Think about things like the linearity assumption in log-odds, handling interactions, multicollinearity, class imbalance, probability calibration, and the tradeoff between interpretability and flexibility.

“This question is broader than it sounds.”

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Citi·Data Scientist·Technical Phone Screen

May 2026

Interviewed for a Data Scientist role at Citibank, technical screen focused heavily on credit risk modeling, specifically LGD. One question but it had a lot of moving parts and I don't think I covered all of them as cleanly as I wanted.

How would you handle missing values in a dataset for LGD modeling? Walk through multiple imputation, model-based imputation, business rule fills, indicator variables, and when you'd actually just leave missingness in the model explicitly.

“This one sprawled.”

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Citi·Data Scientist·Technical Phone Screen

May 2026

SQL-heavy screen for a data scientist role at Citi. One question, pretty deep on window functions and financial metrics. Not a lot of fluff.

Write a SQL query to calculate 12-month rolling default rates by customer segment. You have a table with columns for month, segment, number of defaults, and number of accounts. For each segment and month, compute the sum of defaults over the current and prior 11 months divided by the sum of accounts over the same window.

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

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