Almost ten years ago I was a data scientist who memorized APIs, read papers to find a gap, and scoped every project to what I could personally code. Today I write less code than ever and ship more software than ever — across research, data work, building a company, navigating immigration, and helping my family. This is five short before/after stories with one shared shape: where my thinking used to start, where it starts now, and the skill I quietly stopped exercising in exchange. No architecture diagrams — instead, the human version of a problem many of us are solving in our systems: when producing work gets cheap and verifying it gets expensive, do we upgrade our judgment as fast as our tools? I'll use real examples — from RAG-based legal assistants to building imigrei.pt and Prep Docs to help immigrants face Portuguese bureaucracy—and close with an uncomfortable question. You'll leave having relocated at least one bottleneck in your own workflow, and a little unsure whether you're still good at the part that now matters most.
Marcelo Ventura is a data scientist and AI engineer based in Lisbon. Over nearly a decade he has built ML and data systems across Brazil, Singapore, New York, California, and Central Europe — from loan-forecasting neural nets and cashflow automation to RAG-based GenAI assistants and legal copilots at fintechs and startups. He holds an MSc in Applied Economics and has published research in statistical modeling and econometrics. A Brazilian immigrant himself, he is now Data & AI Engineer at imigrei.pt, where he builds tools — including Prep Docs — that help immigrants navigate Portuguese bureaucracy, and hosts the Imigrantes de Sucesso podcast.
