Data Scientist & Machine Learning Practitioner Focused on applied machine learning, data analysis, and reproducible ML workflows using Python and Linux. Interested in end-to-end ML projects, including model training, API deployment, Dockerization, and CI/CD automation.
- Strong background in mathematics and applied statistics, with experience building and evaluating predictive models.
- Daily Linux user for 10+ years (Debian-based systems), comfortable with development, scripting, and system-level tooling.
- Experience developing Machine Learning APIs and containerized services using Flask, Docker, and GitHub Actions.
- Focus on reproducible ML projects using modern Python tooling (
src/layout, dependency locking, CI pipelines). - Programming experience in Python (primary) and C.
- Former mathematics teacher, with experience explaining complex concepts clearly and rigorously — a skill now applied to documentation and technical communication.
- Python, R, C
- pandas, numpy, scikit-learn, matplotlib
- Jupyter / notebooks (used as exploration tools, not final artifacts)
- Supervised learning (classification, regression)
- Model evaluation and validation
- Separation of training and inference pipelines
- API-based model serving
- Reproducible environments (
pyproject.toml, lock files) - CI/CD with GitHub Actions
- Flask (ML APIs)
- Docker & Docker Hub
- Basic REST API design
- Linux-based deployment workflows
- Linux (Debian, Ubuntu)
- Bash, CLI tooling
- Git & GitHub
- VS Code
- End-to-end Machine Learning projects (from data to deployment).
- API-first ML systems.
- Dockerized ML services.
- CI/CD automation for testing and deployment.
- Clean project structures and professional Python standards.
- Gradually deepening knowledge in MLOps and production ML practices.
- 📧 Email: harrue.ds@gmail.com
- 💼 LinkedIn: https://www.linkedin.com/in/henzo-arrué-muñoz/
- 🐳 Docker Hub: https://hub.docker.com/u/harrueds
- 📊 Kaggle: https://www.kaggle.com/harrueds