Summary
Fernando Caprile is a data scientist with eight years of professional experience and over three years focused on applied machine learning, model fine-tuning, and data engineering in production environments. He blends hands-on expertise with PyTorch/TensorFlow model work, statistical analysis and visualization, and backend skills (FastAPI, async tasks, InfluxDB, MongoDB, MySQL) to ship low-latency, accurate inference systems. Recent projects include refactoring a license-plate/vehicle detector to boost recall without raising inference time and leading a Napari plugin effort to simulate focused beams for high-resolution microscopy. Comfortable in cloud-native and microservices settings (Kubernetes) and collaborative workflows (Git/GitHub/GitLab), he often bridges research-quality methods and production constraints. Collected training in physical sciences informs his quantitative approach and interest in scientifically grounded solutions.
8 years of coding experience
1 year of employment as a software developer
Licenciatura, PHYSICAL SCIENCES, Licenciatura, PHYSICAL SCIENCES at University of Buenos Aires