The statistics problem is the easier half. What separates a data scientist who can run an experiment from one who can build experimentation infrastructure is Delta Lake, MLflow 3.0, and Unity Catalog working together. A walkthrough of the full production pipeline with code.

Most experiments run longer than they need to and produce results nobody trusts. Pre-experiment variance is the hidden cause. CUPED strips it out. A practical explanation of the math and a worked example showing 30-40% variance reduction in practice.

A year into researching adversarial ML and LLM evaluation under Prof. Bowei Xi at Purdue. What the academic literature misses, what actually transfers to production systems, and why the robustness problem is harder than the benchmarks suggest.

The gap between academic DS and production DS is wider than most students expect. What the classroom doesn't teach about messy data, stakeholder communication, and making decisions under uncertainty.