Data Science
Predictive models, statistical analysis, and machine learning applied to problems where the answer is not already in a report. We build and deploy data science solutions on Azure that produce decisions, not just experiments.
Three things we focus on.
Predictive modeling
Regression, classification, forecasting, and anomaly detection, applied to your data and your business problem. Models evaluated rigorously, not just against a holdout set, with drift monitoring once in production.
Machine learning on Azure
Azure Machine Learning for experiment tracking, model registry, and deployment. Pipelines that a data engineer can maintain after the data scientist has moved on.
From notebook to production
Most data science value dies in a notebook. We build the inference endpoint, the monitoring, the retraining trigger, and the feature pipeline that make a model a product.
Whatever shape fits the work.
Two to three weeks to determine whether a specific prediction problem is worth solving and what the data requirements are.
Take a validated use case from training through production endpoint with monitoring and a retraining cadence.
Senior data scientist working alongside your team for a defined window to accelerate a backlog of modeling work.
What we get asked to do.
- Build a demand or sales forecasting model on historical transaction data
- Train and deploy a classification model for customer churn or risk scoring
- Deploy Azure Machine Learning pipelines for experiment tracking and model registry
- Build an anomaly detection model on operational or financial time-series data
- Move a data scientist's notebook model into a governed production inference endpoint
- Add drift monitoring and retraining triggers to an existing production model
- Conduct a feasibility spike to assess whether a prediction problem is solvable with available data
- Build a feature engineering pipeline that feeds multiple downstream models
What we bring to data science.
Production is the requirement
We do not stop at the model. Evaluation harness, inference endpoint, monitoring, drift detection, and retraining triggers are part of every build. A notebook that works in isolation is not a product.
Problem framing before notebooks
Data science engagements fail when they start with a technique instead of a question. We spend real time on problem framing: what prediction, what data, what change in behavior if it works. Only then does modeling begin.
Azure Machine Learning native
Experiment tracking, model registry, pipeline orchestration, and deployment on Azure ML. The scaffolding that makes models maintainable by a data engineer after the data scientist has moved on.
Drift management from the start
Production models degrade. Input distributions shift and the world changes. We instrument for input and output drift from the first deployment so you know when the model is breaking down before the business notices.
What clients typically see.
Ready to talk about data science?
Tell us what you are trying to change. We will either be useful, or point you to who would be.