Data science only matters when it changes a decision. We build models that answer specific business questions, validate them rigorously, and integrate them into the workflows your teams already use.
What we build
Our data scientists combine statistical rigor with domain context. We do not deliver notebooks that gather dust. We deliver models in production with clear ownership and refresh schedules.
- Forecasting models for inventory, demand, revenue, and capacity planning
- Classification models for risk scoring, fraud detection, and segmentation
- Experimentation frameworks for product and marketing teams running A/B tests at scale
- Causal analysis to separate correlation from actionable drivers
Our modeling process
We start with the decision, not the algorithm. Problem framing, data audit, baseline model, iterative improvement, and production validation. Every step includes stakeholder review so the final model reflects business reality.
Tools and methods
Python and R ecosystems, scikit-learn, XGBoost, Prophet, statsmodels, and deep learning where it adds value. We choose simplicity over complexity when a linear model answers the question.