The Walt Disney Company Data Scientist Interview Guide

Three candidates describe Disney Data Scientist interviews that look quite different, so prepare for range rather than one fixed format. The evidence is limited, and each account reflects a single candidate's team and timing.

One candidate describes a hands-on Python screen with the hiring manager that covered Python, SQL and machine learning concepts. They were asked to write a cosine similarity function, and the questions felt applied rather than purely algorithmic. Another candidate was asked to merge overlapping intervals. Their first pass compared each interval only with the previous one and broke on unsorted input. Sorting by start value and comparing against the last merged interval fixed it. Practice writing clean, runnable code and testing it on unsorted and edge-case inputs.

The same fall 2024 candidate who got the cosine question said the next two rounds were behavioral: one with two team members on conflict resolution and disagreements, and one with a manager from a cross-functional team on collaboration. They also recalled a case question with sample data and basic calculations, plus conceptual optimization questions about duals and feasibility with added constraints. That candidate did not advance.

A third candidate went from an external recruiter to a hiring manager who was a PhD economist. They were asked about stakeholder projects, how to forecast demand from a stakeholder's prompt, how to determine pricing, and what to do if a stakeholder wants the numbers nudged. They felt rusty on econometrics. If your target team is analytics or economics oriented, review forecasting, pricing logic and econometrics, and prepare an honest answer about pushback on results. All three reports ended in rejection or no offer, so rehearse concrete stories as carefully as the code.