How I cracked multiple interviews (and the AI/ML strategies that actually worked)

Hey everyone,

I’ve noticed a lot of people here asking how to prepare for Consultant interviews (especially with AI/ML topics becoming more common).
I recently went through the same journey and wanted to share a few things that actually worked for me:

What helped me prepare:

Focusing on AI/ML use-cases instead of algorithms (interviewers cared more about how I’d apply them in a project context). Revisiting core frameworks like SIPOC, MoSCoW, user stories, RACI, etc. Practicing scenario-based questions (e.g. “How would you identify and prioritize ML opportunities for a retail client?”). Preparing 2–3 solid project stories and framing them using STAR.

Actual questions I got asked:

“How would you gather requirements for an ML-based forecasting solution?” “Explain a real-life process where you think AI/ML could improve efficiency.” “What’s the difference between supervised vs unsupervised learning — from a business perspective?”

These might sound basic, but most candidates struggle to articulate a clear business-oriented answer.

If anyone is actively preparing, I found this book which helped me a lot in understanding AI/ML concepts and also helped me to prepare for the interviews.

« The Ultimate AI/ML Guide for Analysts and Consultants – Premium Edition »
(Book link in the first comment)

Happy to share more tips or answer questions if anyone’s interested!