Signs you need this
- Decisions are made on gut feel while the data to inform them sits unused.
- A model exists but nobody monitors it, and nobody trusts it.
- You need scores, forecasts or recommendations that plug into an operational system.
Overview
A model is only useful if it changes a decision. We start from the decision, the data that informs it and the cost of being wrong, then build the modelling and deployment pipeline around that.
Our data scientists and ML engineers cover the full lifecycle: exploration, feature engineering, model selection, hyperparameter tuning, deployment and monitoring, using well-understood tools rather than novelty for its own sake.
How this differs from Statistical Solutions Development: Data Science builds predictive models that ship into systems; Statistical Solutions answers questions with experiments, tests and inference.
What we deliver
- Exploratory analysisA clear picture of what your data can and cannot support before modelling starts.
- Feature engineeringReusable feature pipelines with documentation and tests.
- Predictive modelsClassification, regression and ranking with XGBoost, LightGBM, ensembles and neural nets.
- Recommendation systemsCollaborative and content-based approaches suited to your catalogue and traffic.
- Model deploymentBatch or real-time scoring APIs with latency targets and versioning.
- MonitoringDrift detection, performance dashboards and retraining triggers.
Where it fits
Credit and risk scoringEnsembles on bureau, transaction and alternative data with explainability for regulators.
Churn and attritionPredict who is likely to leave and what intervention is most effective.
Demand forecastingTime-series models for inventory, staffing and capacity planning.
RecommendationPersonalised suggestions that lift conversion without eroding trust.
Technology we use
Chosen per project. We are vendor-neutral and will recommend what fits your constraints.
PythonPandasNumPyScikit-learnXGBoostLightGBMOptunaMLflowFastAPIAirflowDatabricks
How we work
Decision framing
Which decision, who makes it, what does a better one look like?
Data audit
Availability, quality, leakage risks and labelling needs.
Modelling
Baselines first, then tuned models, evaluated against business metrics.
Deployment
Serve, monitor and hand over with documentation and training.
Typical first engagement
A decision-framing and data-audit sprint, ending in a baseline model and a clear statement of what a production model can achieve.
Common questions
Which is better: a simple model or deep learning?
The simplest model that meets the target. Gradient-boosted trees win most tabular problems; we escalate only when the data demands it.
How do you handle explainability?
SHAP-style attributions, reason codes and monotonic constraints where regulation or trust requires explanations.
Do you take over an existing model?
Yes. We audit, document, add tests and monitoring, and improve it incrementally.
