AI and data roles
When a company says they need to hire for AI, they usually mean five very different jobs. A data engineer builds and maintains the pipelines that move data from source systems into usable formats — they are the plumbers. A data analyst creates reports and dashboards that help people understand what is happening — they are the translators. A data scientist builds statistical and machine learning models that find patterns and make predictions — they are the researchers. An AI engineer builds production systems that use AI models in real applications — they are the builders. And an ML ops engineer keeps those production AI systems running reliably — they are the operators. Hiring the wrong role for your problem is one of the most expensive mistakes companies make.
Go deeper
The confusion is understandable because these roles overlap and the titles are inconsistent across companies. A 'data scientist' at one company might do what another company calls 'analytics engineering.' An 'AI engineer' at a startup might do everything from data pipelines to model deployment.
Here is a practical way to think about it: if your problem is that data exists but is trapped in silos and hard to access, you need a data engineer. If your problem is that people cannot get answers from the data you have, you need an analyst. If your problem is that you want to predict outcomes or find hidden patterns, you need a data scientist. If your problem is that you have a working model but need it embedded in your product or workflow, you need an AI engineer.
The emerging role is the AI-augmented generalist — someone who uses AI tools to do work that previously required specialists. A product manager who uses Claude to write SQL queries. A marketer who uses AI to segment audiences and generate campaign variations. A finance leader who uses AI to build forecasting models. These are not data professionals, but they are increasingly doing data work. The question for most companies is not whether to hire specialists or generalists, but what ratio — and which specialized capabilities you truly need in-house versus what you can access through AI tools and external partners.