Case Study - Data Science Engagement

Overview

We completed a bespoke analytics project for Cenovus Energy. The engagement focused on cleaning large-scale operational data and training predictive models to guide strategic decisions.

Our team built a secure pipeline for data ingestion, feature engineering, model training and reporting.

We gathered operational metrics from multiple facilities, transforming raw logs and sensor readouts into structured datasets. This required handling irregular sampling, missing values and inconsistent units.

Python-based ETL jobs produced new features for forecasting and predictive maintenance. We evaluated gradient-boosted trees and recurrent neural networks across cross-validation folds to find the best fit for each task.

The final models were containerised and deployed to the cloud with dashboards that surfaced daily insights for engineers. Automated reports freed up the team to focus on high-value work.

What we did

---