Nethmika Kumarasiri
I'm a Computer Science and Engineering undergraduate at the University of Moratuwa. I use Python, statistics and machine learning to turn data into useful, explainable systems, supported by full-stack, backend and DevOps skills.
Selected work
Projects spanning data, intelligent systems and reliable product engineering.
Full-stack · Backend · Deployment
A clinic-management application connecting appointment scheduling, doctor workflows and administrative operations.
Next.js · TypeScript · PostgreSQL · Redis · Flyway · Docker · GitHub Actions · Azure
About
My focus is the full data-science lifecycle: understanding a problem, preparing and exploring data, evaluating models honestly, and communicating results clearly.
I specialize in Data Science, Artificial Intelligence and Machine Learning, supported by practical backend, cloud and DevOps skills. I enjoy connecting models to reliable applications and building the pipelines that keep their data useful.
When I build something, I want to understand the decisions behind it. That means comparing alternatives, testing edge cases, measuring results and documenting limitations.
Computer Science and Engineering Undergraduate
Specialization: Data Science / AI / ML
Skills
Data preparation, statistical analysis, model evaluation and reproducible experiments.
Python · SQL · NumPy · Pandas · Matplotlib · scikit-learn · PyTorch
User-facing applications, APIs, authentication and background processing.
Java · Spring Boot · TypeScript · Next.js · React · FastAPI · Go
Data modeling, transactions, query performance and reliable pipelines.
PostgreSQL · Redis · MongoDB · Kafka · dbt · Airflow
Repeatable environments, automated delivery and deployment troubleshooting.
Linux · Docker · GitHub Actions · Nginx · Terraform · Kubernetes · AWS · Azure
Automated tests, meaningful metrics and failure diagnosis.
JUnit · pytest · Playwright · Testcontainers · Prometheus · Grafana
Approach
Define the user, requirements and success criteria before choosing tools.
Test the important rules, permissions and failure cases.
Use baselines and reproducible measurements to guide changes.
Consider deployment, monitoring, backups and rollback.
Document what I chose, what I rejected and what still needs work.
Contact
I'm interested in internships and collaborations in Data Science, AI and Machine Learning—especially work that connects rigorous analysis and practical models with reliable, usable applications.
Based in Sri Lanka.