Currently Lead Data Scientist & AI at Auror, focusing on retail crime prevention through data science and machine learning.
At Auror, I lead the data science team that's revolutionizing retail crime prevention. We process millions of crime events daily, using machine learning to identify patterns and prevent retail crime before it happens.
Key areas:
- Building scalable ML pipelines
- Implementing real-time prediction systems
- Improving model monitoring and maintenance
- Mentoring team members in ML best practices
Core Stack:
- Python (Pandas, NumPy, scikit-learn)
- PyTorch & TensorFlow for deep learning
- DBT & Snowflake for data processing
- Dagster for workflow orchestration
- Azure & GCP for cloud infrastructure
- Docker & Kubernetes for containerization and orchestration
- Git for version control
- Jupyter Notebooks for experimentation
- VS Code for development
- FastAPI for building APIs
- Uv for dependency management
- ruff for code quality and linting
Data Storage:
- PostgreSQL
- SQL Server
- Snowflake
- Neo4j for graph data
Feel free to reach out if you want to discuss data science, machine learning, or technology in general!