I'm a Data Scientist at Amazon, with a background that spans some of the most selective ML/DS environments in the industry — including internships as a Data Scientist and Applied Scientist at Amazon, a research stint at ISRO's Space Application Centre, and Samsung PRISM. Over the past few years I've worked across the full spectrum of applied ML — from research-driven problem solving to building and shipping models at scale in a live, high-stakes business. I know what it actually takes to break into these roles because I've been on both sides: the intern trying to prove myself, and now the person on teams that make these hiring calls. I started mentoring because the path into ML/DS roles at top companies is rarely explained clearly — most advice online is generic. I focus on what actually moves the needle: how to position your background (even a non-traditional one), what these interviews really test for, and how to build a portfolio that gets noticed. If you're trying to break into Data Science, ML Engineering, or Applied Science — especially at a company like Amazon — and want a straight, specific answer instead of a LinkedIn platitude, book a call.