I’m a Data Scientist & Senior Solution Architect combining 15+ years in enterprise IT with practical Python, machine learning and forecasting project experience.
My background spans production SQL development, data modelling, systems integration and SaaS delivery, alongside electricity distribution innovation at UK Power Networks. I recently completed the University of Cambridge Professional and Continuing Education Career Accelerator in Data Science with Machine Learning & AI, delivered in collaboration with FourthRev.
I enjoy taking problems from requirements through to working solutions—preparing data, building and evaluating models, connecting systems and making the results useful.
- Student dropout prediction — Comparing XGBoost and neural networks across enrolment, attendance and assessment stages, with model interpretation, a runnable notebook workflow and a business report.
- Ship-engine anomaly detection — Exploring candidate anomalies using IQR, One-Class SVM and Isolation Forest, with model comparisons and a stakeholder report.
- Customer segmentation — Segmenting 68,300 customers using K-means, clustering diagnostics and business interpretation, with a supporting report.
These repositories present academic project work, with methods, results and limitations documented.
Data science: Python, pandas, NumPy, scikit-learn, XGBoost, time-series forecasting, feature engineering and model evaluation.
Engineering: SQL Server, T-SQL, relational data modelling, REST/SOAP integrations and solution architecture.
I’m preparing further completed projects for publication:
- NLP sentiment analysis — Exploring text using topic modelling and language models.
- Time-series forecasting — Comparing statistical and machine-learning approaches, with backtesting and evaluation.
Before sharing, I’m reviewing data permissions, removing identifying or confidential information, and adapting examples to use public or synthetic data where needed. Employer-project material will only be included where permitted.