I'm a final-year BSc (Hons) IT undergraduate specializing in Data Science at SLIIT, Sri Lanka, with a strong passion for building intelligent, data-driven systems - from end-to-end data warehouses and BI dashboards to multi-agent AI workflows and predictive ML pipelines.

I enjoy bridging the gap between raw, complex data and clear, actionable insights. Whether it's engineering robust ETL pipelines, designing dimensional models, or deploying ensemble ML models, I approach every problem with analytical rigour and a product-minded perspective.

• Location: Malabe
• Degree: BSc (Hons) IT — Data Science Specialization, SLIIT (Expected 2027)
• Currently focused on: Machine Learning, Data Engineering, and AI-powered application development
• Open to: Data Science, ML, AI, and Data Engineering internship opportunities

Languages

Data Science & Machine Learning

Business Intelligence & Visualization

Data Engineering

Databases

Cloud & DevOps

• Agentic AI systems and multi-agent orchestration frameworks (CrewAI, LangGraph)
• Advanced feature engineering and model interpretability techniques
• Cloud-native data pipeline design on AWS and Azure
• Real-time data streaming and modern data lakehouse architectures

End-to-end DW&BI solution processing 251,000 London Fire Brigade incident records across a full pipeline — from multi-source ingestion to OLAP reporting and interactive Power BI dashboards.

SQL Server SSIS SSAS Power BI DAX T-SQL Star Schema Dimensional Modelling

Multi-agent AI pipeline built with CrewAI and GPT-4o-mini to automate a complex legal research workflow, integrating live data from the CourtListener API across a React.js frontend.

Python CrewAI GPT-4o-mini MongoDB React.js REST APIs Microservices

Large-scale statistical study across 8,325 paired model comparisons on 19 benchmark datasets, applying hypothesis testing (α = 0.05) to evaluate the performance advantages of ensemble methods — achieving an 87.2% ensemble win rate.

Python Scikit-learn Statistical Analysis Hypothesis Testing Pandas Seaborn

End-to-end ML pipeline for real-world Airbnb price prediction, including data cleaning, feature engineering, feature importance analysis, and model benchmarking using RMSE and MAE.

Python XGBoost Scikit-learn Pandas NumPy Matplotlib Jupyter Notebook

Full-stack MERN application featuring booking analytics, time-slot optimisation, conflict prevention, automated email notifications, and admin dashboards with PDF reporting.

MongoDB Express.js React.js Node.js Chart.js