Practical models and test programmes tied directly to commercial metrics.
Experimentation, Forecasting & Applied ML
Frame the decision, design robust experiments or models, and keep them accountable in production.
Where each engagement starts
- Quantify the decision, counterfactual, and target KPIs with stakeholders
- Audit data availability, labelling, and historic experiments to ground the roadmap
- Design delivery phases that balance exploration, productionisation, and enablement
Outcomes
- Fraud, pricing, or lifecycle models that reach production with measurable ROI
- Automated experiment read outs and forecasting that finance and product can trust
- Playbooks for ongoing monitoring, retraining, and stakeholder communication
Stack Python, scikit learn, XGBoost, ClickHouse, dbt, Superset, ThoughtSpot