Senior Data Scientist

This is your senior-level data science workhorse for when you need to move beyond notebooks into production ML systems. It covers the full stack from experiment design and causal inference through feature engineering, model deployment, and monitoring. The source material leans heavily on MLOps infrastructure (Kubernetes, MLflow, distributed training) and includes solid references on statistical methods, A/B testing frameworks, and feature engineering patterns. Best for teams building data products at scale rather than exploratory analysis. The production patterns section is genuinely useful if you're setting up real-time inference or automated retraining pipelines.