Data scientist who ships. I work at the intersection of graph analytics, machine learning, and the infrastructure needed to put a model in production.
My public work is small and deliberate — a handful of repos I actually finished rather than a pile of half-starts.
- Graph data science — Neo4j, NetworkX, node embeddings, and centrality algorithms that run against a live database instead of an in-memory copy
- ML in production — training a model, wrapping it in an API, containerizing it, and load-testing the result
- Interview prep — working through algorithm problems for muscle memory, not for show
| Repo | What it is |
|---|---|
income_detection_api |
End-to-end MLOps: train a CatBoost census-income classifier, serve it with FastAPI, containerize, deploy to Kubernetes, load-test with Locust |
neo4ds |
Notebooks for learning Neo4j graph data science — a beginner's path plus a Titanic knowledge-graph embedding demo |
networkx-neo4j |
⭐ My fork of neo4j-graph-analytics/networkx-neo4j, a NetworkX API for Neo4j GDS. Contributions are merged upstream |
notes |
Data science scratch notebooks — vectorization, Jupyter tooling, Python language notes |
Languages — Python, SQL, Bash Data & ML — pandas, NumPy, scikit-learn, CatBoost, Jupyter Graphs — Neo4j, Neo4j Graph Data Science, NetworkX, GDS algorithms Serving & infra — FastAPI, Docker, Kubernetes, AWS (ALB, ASG, RDS, S3, Lambda, DynamoDB, CloudFront, Route 53), Terraform, CloudFormation
- LinkedIn — if you'd rather talk about work in detail
All public repos are MIT licensed unless noted. networkx-neo4j is Apache-2.0, inherited from upstream.