Forecasting, segmentation, churn, pricing, anomaly detection — the questions where the answer changes a decision and someone will ask how you arrived at it. We build models that are accurate enough to be useful and explainable enough to be argued with, then put them somewhere they actually run.
Overview
Models that reach production
Most data science never ships. It lives in a notebook, produces a number nobody can reproduce, and quietly stops being run. We treat a model as software from the first week: versioned data, a training pipeline that can be re-executed, evaluation against a held-out set the business agrees is fair, and a serving path with monitoring on it. If it cannot be re-run next quarter and give the same answer, it is not finished.
What we deliver
The unglamorous parts decide the result
Model choice is rarely the bottleneck. Feature quality, leakage, class imbalance and a test set that does not reflect reality are what separate a model that works in a notebook from one that works in the business. We spend the time there, and we say plainly when the data will not support the question being asked.
- Framing: turning a business question into something measurable
- Feature engineering on top of the warehouse, not beside it
- Honest evaluation — held-out sets, baselines, and the cost of being wrong
- Explainability where a decision affects a person or a price
- Deployment, monitoring and drift detection, not a handover of notebooks
Why it matters
When it is worth doing
A model earns its keep where a decision is made often, the outcome is measurable, and a small improvement compounds. Where the decision is rare or the data thin, a clear report beats a model and we will say so. The cheapest engagement is the one that establishes there is no model worth building.
Delivered in
How we engage
We start with a baseline — often something deliberately simple — so every later model has something to beat. It keeps the conversation about whether the work is paying rather than about how sophisticated it is.
Workflow
Our Data Science Workflow.
- 01
Question & Baseline
- Turn the business question into a measurable target
- Agree what a useful improvement would look like
- Establish a simple baseline to beat
- Confirm the data can actually support the question
- 02
Features & Data Preparation
- Feature engineering against the warehouse
- Leakage checks and temporal validation
- Class balance, sampling and missing-data strategy
- Reproducible datasets under version control
- 03
Modelling & Evaluation
- Candidate models compared against the baseline
- Held-out evaluation with the cost of error made explicit
- Explainability appropriate to the decision being made
- Review with the people who will act on the output
- 04
Deployment & Monitoring
- Serving path: batch scoring or an endpoint
- Drift, performance and data-quality monitoring
- Retraining cadence and ownership
- Documentation the next analyst can pick up
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