Statistics and machine learning
Predictive models built to be valid, not merely to fit well in sample. This is the area where apparent performance is most often mistaken for real performance.
- Predictive modeling and model selection on explicit criteria, not by habit.
- Uncertainty quantification: intervals you can actually decide on.
- Validation design: out-of-sample, by horizon, with information leakage controlled.
- Independent review of third-party models, machine learning ones included.
- Deployment with drift monitoring and reproducible retraining.