Data science & machine learning
Experimentation & causal inference
The design of experiments and the analysis that separates a real effect from a seasonal one.
5–9 weeksTypical duration
The problem this solves
Every change is declared a success, and the numbers that prove it were chosen after the change shipped.
What you receive
Artefacts you can hold, and that you can accept or refuse — never a list of activities.
- An experiment design standard, covering power, duration and the metric agreed before launch
- The analysis pipeline, producing the same verdict for everyone who runs it
- Causal analysis for the changes that cannot be randomised
- A written review of your three most consequential recent claims
Also in data science & machine learning
Decision modelling & forecasting
Demand, risk or capacity models built against the decision they inform rather than against a leaderboard metric.
MLOps platform
Feature store, model registry, serving and monitoring, so a model is deployable by the team that built it.
Model risk & validation
Independent validation, documentation and challenge, in the form a regulator or an audit committee expects.