Advanced Analytics & Decision Science

Experimentation, Forecasting & Applied ML

Frame the decision, design robust experiments or models, and keep them accountable in production.

Where the work 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

Intended outcomes

Fraud, pricing or lifecycle models evaluated against agreed decision criteria
Automated experiment read outs and forecasting that finance and product can trust
Playbooks for ongoing monitoring, retraining, and stakeholder communication

Tools in context

Python, scikit learn, XGBoost, ClickHouse, dbt, Superset, ThoughtSpot

Put the next question to work.

Bring the question, the available evidence and the constraints. Speak directly with the practitioner who will do the work.

Talk through an analytics problem