Data Scientist / Analytical Software Engineer PhD candidate in Physics with 5+ years of experience building reproducible analytical workflows, statistical modeling pipelines, and data-intensive software systems.
- Data Science
- Machine Learning
- Analytics Engineering
- AI Tooling
- Scientific and Analytical Software
- Data Science & Machine Learning
- Statistical Modeling & Time Series
- Experimentation & Evaluation Frameworks
- Analytics Engineering
- Reproducible Workflows
- Python, SQL, Julia
- Docker, CI/CD, Cloud Deployment
Privacy-first analytics workflow on real Garmin wearable data. Python, SQL, DuckDB, scikit-learn, statistical validation, time-aware modeling and dashboard-oriented analytics.
End-to-end computer vision project with reproducible experiments, robust model selection, calibration analysis, FastAPI serving, Docker and Azure deployment.
Simulation and experimentation platform for a stochastic tactical decision environment with exact references, benchmark evaluation, heuristic agents and deterministic replay.
Julia framework for reproducible computational experiments, statistical diagnostics and uncertainty-aware analytical workflows.
Backend control plane for reproducible computational runs with FastAPI, PostgreSQL, Redis + RQ, and a real external runner integration.
Languages: Python · SQL · Julia · C/C++
ML & Data: pandas · NumPy · SciPy · scikit-learn · PyTorch
Engineering: Docker · GitHub Actions · FastAPI · Linux
Analytics: DuckDB · PostgreSQL · Statistical Testing
- First-author peer-reviewed publication (A&A, 2024)
- Built analytics workflows on 23-year irregular time series
- Developed reproducible experiment frameworks used in PhD research
- Deployed ML services using FastAPI, Docker and Azure