An optimistic and tenacious person, passionate about learning, solving real-world problems, and turning data into clear, practical insights.
I build machine learning systems, work with real-world data, and teach Statistics to undergraduate and postgraduate students, making technical ideas practical, clear, and useful.
Advanced study across machine learning, NLP, data analytics, time series and responsible AI, with dissertation work focused on UK fire-safety regulatory guidance comparison.
Graduated with Distinction, building the statistical foundation for later work in predictive modelling, analytics, machine learning and teaching.
Taught Statistics to 1,000+ students across BSc, BBA, MBA, MPA and MBS programmes using practical, clear explanations.
Assisted with basic share-market related data work, including organising information, preparing data and supporting simple analysis and reporting.
My skills combine data science, machine learning, analytics and clear communication to solve practical problems.
Comfortable building data workflows, cleaning datasets and analysing information using core programming and database tools.
Experience applying supervised and unsupervised machine learning methods, model evaluation and explainability in project work.
Able to work with textual data, embeddings and graph-based relationships for insight generation and analysis.
Solid statistical foundation for analysis, forecasting and performance evaluation across data science tasks.
I focus on turning analysis into clear visuals, usable outputs and understandable stories for different audiences.
Alongside technical ability, I bring communication, teaching and thoughtful problem-solving to collaborative work.
Here are selected projects in machine learning, analytics, NLP and responsible AI that show both technical depth and practical problem-solving.
Built regression and classification pipelines to predict product lifespan and manufacturing defects, with tuning, feature selection and SHAP-based explainability.
Used segmentation, rolling averages, heatmaps and anomaly-focused analysis to explore visitor patterns and communicate performance trends clearly to stakeholders.
Developed an NLP system to analyse fire safety regulatory guidance collected from England, Wales, Scotland and Northern Ireland, supporting version comparison and cross-jurisdiction clause comparison across the UK.
Combined graph analytics and machine learning to identify suspicious transaction patterns and surface risky relationships in connected financial data.
Compared manual and auto-selected ARIMA models after stationarity testing to evaluate forecasting performance on long-run market data.
Assessed the Metropolitan Police Gangs Violence Matrix (GVM) in London, focusing on fairness, transparency, accountability and its impact on affected communities.
I’m open to data science opportunities, collaborations, teaching and project discussions.
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