Data science & machine learning
Decision modelling & forecasting
Demand, risk or capacity models built against the decision they inform rather than against a leaderboard metric.
6–12 weeksTypical duration
The problem this solves
You have forecasts, and the people who make the decisions do not use them, because the forecast does not answer the question they are actually asking.
What you receive
Artefacts you can hold, and that you can accept or refuse — never a list of activities.
- The decision framed explicitly, including who makes it, when, and with what alternatives
- A model built and back-tested against that decision's own loss, not against a generic accuracy score
- The uncertainty communicated in a form the decision-maker can act on
- A monitoring lane that detects when the model has stopped being right
Also in data science & machine learning
MLOps platform
Feature store, model registry, serving and monitoring, so a model is deployable by the team that built it.
Experimentation & causal inference
The design of experiments and the analysis that separates a real effect from a seasonal one.
Model risk & validation
Independent validation, documentation and challenge, in the form a regulator or an audit committee expects.