* Technical interviews was an average complexity for both python and SQL * The final interview was 4 sections: 1. Consulting: Quick and generic. Didn’t see how you can prepare beyond STAR stories of your past work on your resume. 2. Machine Learning: Was supposed to conceptual ML techniques questions but interviewer made it a case. 3. Business Case: Was supposed to be a case but it was just a discussion of my past experience. 4. Leadership: Typical behavior
LP/MILP formulation for manufacturing optimization
Tell me about your previous projects
Python (map, reduce, functions, classes) SQL (join, sorting, grouping) Project presentation (describe and present a project to panel) Situational and motivational
They asked how I would solve a user segmentation situation.
All of the questions were based on the project from last job.
Implement k-means from scratch using the preferred language
The questions were about my background, nothing technical at the recruiter stage.
Asked via Technical Assessment/Case Study Prompt then defended / elaborated upon in Team Interview.
Considering smoking causes cancer, what are the statistical methods we can use to deduce this/
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