👋 Hi, I’m Stuart Houston
Senior-level Data Scientist | MLOps & AI Infrastructure
I’m a Data Scientist focused on building production-ready, modular ML pipelines that demonstrate value quickly while laying the foundations for scalable, secure deployment.
As the sole Data Scientist at CNOOC International, my work involves more than just model development it’s about proving what’s possible, showing where Data Science creates value, and designing workflows that can evolve smoothly from proof-of-concept to production once the infrastructure is ready.
Much of my work uses controlled-access industrial data governed by internal standards, so the public repositories here serve as case studies that reflect similar challenges: messy, real-world data, feasibility testing, and designing systems that are robust enough to scale.
- NASA CMAPSS dataset → Remaining Useful Life prediction
- End‑to‑end workflow: feature engineering → model training (LightGBM/XGBoost) → MLflow tracking → FastAPI service → Streamlit dashboard
- Structured with Cookiecutter DS template and clear modular organisation to support testing and reproducible workflows.
- Business framing: risk classification (high/medium/low) for asset management decisions
- Computer vision + OCR pipeline for legacy engineering diagrams
- YOLOv5 for symbol detection, EAST for text detection, Tesseract for OCR
- Streamlit app for interactive validation
- Evaluation with precision/recall/mAP across 19 classes
- Future work: Dockerisation, MLflow integration, CI/CD
- Designing production-ready ML pipelines that balance quick experimentation with long-term scalability
- Applying MLOps principles such as reproducibility, version control, and experiment tracking to improve workflow consistency
- Framing proof-of-concepts in business terms, connecting technical insights to operational value and future deployment potential
- Building AI capability from the ground up, including environment standards, governance frameworks, and best-practice workflows
- Always open to discussing senior DS roles , or opportunities to drive AI maturity at scale.