1. Real-world problem solving and business impact - Document clear metrics and outcomes, like "Reduced customer churn by 15% through implementing an early warning system" rather than just describing the technical implementation.
2. End-to-end ownership - Show your ability to handle the full ML lifecycle from data collection and cleaning through deployment and monitoring. Include challenges faced and how you overcame them.
3. Engineering best practices - Demonstrate production-level code quality, testing practices, and MLOps skills like model monitoring and retraining pipelines.