Applied ML jobs

Applied ML is machine learning with a business metric attached: fraud models at fintechs, demand forecasting in logistics, pricing at marketplaces, triage at health startups. The model is often gradient-boosted trees rather than anything with attention layers, and shipping it means winning arguments with domain experts who have watched models fail before.

XGBoost and scikit-learn still carry a startling share of production ML, joined by feature stores and whatever the company's warehouse dictates. The best applied roles hand you a problem end to end — data, model, deployment, and the dashboard the VP checks. By employer count it's the widest pool of ML jobs, and the most geographically spread.