Data science that changes a decision
A model that nobody acts on is an expensive hobby. We start from the decision you are trying to make, then work backwards to the data — and we build the pipeline so the answer keeps arriving after we leave.
Comprehensive data science solutions
From the warehouse underneath to the model on top — and the honest conversation about which of those you actually need.
Data Engineering & Modelling
The unglamorous 80% that everything else stands on. A model on top of an untrustworthy table is worse than useless — it is confidently wrong.
- Dimensional models with tests and lineage in dbt
- Snowflake and Databricks warehouse design
- One agreed definition per metric
- Pipeline monitoring before the CFO notices
Machine Learning Models
Models built to be maintained, not demoed once. Evaluated on every change so you know when they regress.
- Churn and propensity scoring
- Risk and fraud models
- Recommendation engines
- Retraining and drift monitoring
Forecasting & Planning
Demand, capacity, and revenue forecasts that planners actually act on, with the uncertainty stated rather than hidden.
- Demand and inventory forecasting
- Capacity and workforce planning
- Scenario modelling
- Confidence intervals people understand
Business Intelligence
Dashboards leadership actually opens, because the numbers on them can be trusted and everyone agrees what they mean.
- Executive and operational reporting
- Self-service semantic layers
- KPI definition and governance
- Usage tracking on the dashboards themselves
Experimentation
Knowing whether the thing you shipped actually worked — and being able to prove it to a sceptic.
- A/B and multivariate testing
- Causal inference where a test is impossible
- Power analysis before you launch
- Guardrail metrics
Advanced Analytics
The harder problems: optimisation, anomaly detection, and the questions a dashboard cannot answer.
- Optimisation and pricing
- Anomaly and outlier detection
- Customer segmentation
- Geospatial and network analysis
Why choose our data science team
The difference between a model that ships and one that dies in a notebook.
We start from the decision
The first question is never "what data do you have?" It is "what will you do differently if the answer is X?" If there is no answer, there is no project — and we will tell you that in week one, not month six.
We do the boring part properly
Tested, documented, lineage-tracked models in the warehouse. Most of the value in a data engagement is here, and most vendors skip it to get to the interesting bit.
We hand it over
Pairing and knowledge transfer are part of the engagement, not an upsell. The test of our work is whether your team can maintain it after we leave.
Our data science process
A systematic approach to turning your data into decisions people act on.
Decision Discovery
What decision are you trying to make, and what would change your mind?
Data Foundation
Clean, test, and model the data so the answer can be trusted.
Build & Evaluate
Model built against a real evaluation set, scored on every change.
Deploy & Hand Over
Into production, monitored for drift, and handed to your team.
Start from the decision, not the dataset
The first question we ask is not "what data do you have?" It is "what will you do differently if the answer is X?" If there is no answer to that, there is no project — and we would rather tell you that in week one than bill you for six months.
The unglamorous 80%
Most of the value in a data science engagement is analytics engineering: clean, tested, documented models that everyone trusts. We do that work properly, because a brilliant model on top of an untrustworthy table is worse than useless — it is confidently wrong.
- Dimensional models with tests and lineage in dbt
- Warehouse design in Snowflake or Databricks that does not surprise you at invoice time
- A single definition of every metric that matters
- Monitoring so you find out about a broken pipeline before the CFO does
Questions we get asked
Do you work with our existing warehouse?
Yes. We work in Snowflake, Databricks, BigQuery, Redshift, and plain PostgreSQL. We do not require a migration to be useful.
Can you train our team?
Yes, and we prefer to. Pairing and handover are part of the engagement, not an upsell.
Ready to unlock your data's potential?
Tell us the decision you are trying to make. We will tell you honestly whether your data can support it.