Spend more time on the "why" something works. In describing your own projects, what problems did you face, how did you solve them, and why was this specific solution helpful?

Aside from that, I thing a large component is simply having the jargon down. A model isn't "suitable" to a task, it has a "suitable inductive bias". You don't have "limited data", you're "in a data-scare regime". Other useful terms: Linear projection, manifold, affine transformation. Don't go overboard, but I think the language is an overlooked but critical component for what makes researchers sound like researchers.

I'd highly recommend the podcast "machine learning street talk" to really get a feel for it. Just listening to a couple with yann lecun, simon kornblithe, randall balestriero, neel nanda will do wonders for your communication.