Citi Data Scientist Interview Guide

Candidates describe the Citi Data Scientist interview as fundamentals-heavy and resume-driven. Across three Data Scientist reports, interviewers asked how well you understand a concept, not just whether you can name it.

One candidate was asked about linear regression, ROC, AUC, specificity and F1, and how Random Forests work. They were also asked about comfort with Python, R and SAS. Another reported autocorrelation, ACF and PACF plots, Monte Carlo simulation, decision trees, random forests and confusion matrices. That candidate also saw RAG systems, vector databases, SQL and Python OOP. A third described one SQL question, one pandas question (finding the song that ranked first most often), a root-cause-analysis exercise, and a close walk-through of past projects.

Project discussion came up in two of these reports. One candidate said interviewers cared more about the problem solved, the method and the findings than the tech stack. Prepare two or three projects you can explain end to end, including why you chose your approach and what you would change.

Coding looks like a smaller share. One candidate called the algorithm questions straightforward, mentioning linked lists and basic data structures.

Structure varied. One candidate had HR, then two teammates, then a manager, with an offer inside a month. Another went through 2-3 technical rounds, an HR round and a four-interview superday. Two of the three Data Scientist candidates in this set reported compensation conversations late in the process, so check your expectations early. All three reported offers.