Data Science.
Customer analytics, NLP, causal inference and forecasting — engineered into decisions, not stranded in a notebook.
Analytics that changes a decision.
A data-science project we like has a clear decision-owner, a clear counterfactual, and a budget that says "yes" or "no" to it. A data-science project we walk away from has none of those.
Our scientists come from industry, research and consulting — and they're paired with engineers so the model that wins in a notebook also runs in production.
What we do.
Customer analytics
Segmentation, lifetime value, propensity, journey analytics.
NLP & text analytics
Classification, extraction, summarisation, sentiment — production-grade.
Causal inference & experiments
Quasi-experiments, uplift modelling, synthetic control. Because correlation is not enough.
Forecasting
Demand, financial, risk, staffing — probabilistic not point.
Optimisation
Pricing, routing, allocation, staffing — linear, MIP, heuristic.
Decision systems
Model + rules + human-in-the-loop + monitoring, wired into your process.
Problem-first, model-second.
Framing
Decision, counterfactual, metric, success bar.
Exploration
Data profiling, baseline, feasibility spike.
Modelling & ship
Model bake-off, engineering, integration.
Measure
A/B, causal monitoring, attribution.
- ✓Analytical insightReadable, actionable, quantified.
- ✓Production model or decision systemIntegrated into your process.
- ✓Experimentation planA/B or causal, with power analysis.
- ✓Knowledge transferNotebook, code, documentation, paired sessions.
- ✓MonitoringOutcome drift, not just input drift.
- ✓Business-case evidenceBefore/after, with statistical validity.
Tools & frameworks we use.
A real engagement.
Retailer — causal uplift model adds £2.3m/yr in promo ROI.
Uplift modelling replaced response modelling for promotional targeting. Causal-valid A/B measurement showed £2.3m/yr additional profit on the same promotion budget.
Read full case study