Every model used in a consequential decision carries risk: the risk that it is wrong, misapplied, or quietly decaying. Model validation is the independent discipline of finding those problems before reality does. It is my sharpest area of practice.
Note: I discuss regulatory concepts such as SR 11-7 here in general, public-knowledge terms only. Nothing on this site describes any employer's internal processes or proprietary work.
Validation philosophy
Validation is not a rubber stamp at the end of development — it is an independent, skeptical review of the whole modeling enterprise. The validator's job is to try to break the model intellectually: to find the assumption that doesn't hold, the data that leaks the future, the scenario nobody tested. A validation that can't fail isn't a validation.
Conceptual soundness
The most skipped step in validation, and the most important. Before any backtest is run: does the model's theory make sense? Are the assumptions documented and defensible? Is the methodology appropriate for the problem, or just familiar? A model can be mathematically flawless and still wrong about the world. Read Validating Models Like a Skeptic.
Ongoing monitoring
Models rot. Populations shift, relationships decay, feedback loops form. Validation doesn't end at approval — production monitoring with stability metrics, performance drift detection, and clear escalation thresholds is what keeps an approved model honest. More in Detecting Model Decay: Ongoing Monitoring in Production.
Governance & documentation
A model nobody can understand is a model nobody can govern. Good documentation records what was decided, what was assumed, what was tested, and what the limits of the model are — written for the reviewer who has to challenge it, not the developer who already believes it. On the regulatory backdrop, see the LLM evaluation harness.
Related projects
- Clinical Risk Predictive Model — a validation-heavy ML case study: stacked ensembles, R² skepticism, residual bias analysis