Data Science

We help businesses move from descriptive analytics — understanding what happened — to predictive and prescriptive analytics: understanding what is likely to happen next and what to do about it. Our data science work is applied and practical, focused on models that operate reliably in production, not just in notebooks.

What We Offer

• Predictive modelling — forecasting, demand prediction, churn modelling
• Natural language processing — text classification, sentiment analysis, entity extraction
• Recommendation systems — personalised suggestions based on behaviour and context
• Anomaly detection — identify unusual patterns in operational or financial data
• MLOps — model training, versioning, deployment, monitoring, and retraining pipelines
• Statistical analysis and hypothesis testing for product and business decisions

How We Work

We frame every data science engagement around a specific business question with a clear definition of success before any modelling begins. We treat model deployment as a software engineering problem — with proper versioning, monitoring for data drift, and retraining triggers — so models stay accurate over time rather than degrading silently.

Technologies

Python, scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow; MLflow and Weights & Biases for experiment tracking; Sagemaker, Vertex AI, and Azure ML for cloud-based training and deployment.

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