Summary
Javier Domínguez is a Senior Data Scientist with 11 years of experience building and shipping ML and deep learning systems to production, currently applying his expertise at Hudl from Pontevedra, Spain. He has led end-to-end ML work—data and model pipelines, CV model training, model serving, and infrastructure automation—across sports analytics and data-crowdsourcing companies like StatsBomb and DefinedCrowd. Comfortable across the stack, he combines Python-first model development (PyTorch, scikit-learn), MLOps tooling (Kubeflow, KServe, MLflow), cloud platforms and streaming/datastores to deliver scalable, observable solutions. Javier pairs hands-on engineering with stakeholder collaboration and team mentoring, translating product needs into robust pipelines and APIs. His background uniquely blends formal AI research training (PhD work in AI) with musical discipline from conservatory-level violin studies, reflecting strong pattern recognition and collaborative instincts. He’s known for pragmatic, production-focused ML—“shipping ML to prod”—and for improving CI/CD, monitoring, and deployment practices that keep models reliable in real-world use.
11 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD Artificial Intelligence, Doctor of Philosophy - PhD Artificial Intelligence at Universidad de Oviedo
Master's degree Music performance Violin, Master's degree Music performance Violin at Erasmushogeschool Brussel
Engineer's degree Ingeniería informática, Engineer's degree Ingeniería informática at Universidad de Santiago de Compostela
Bachelor's degree Music performance Violin, Bachelor's degree Music performance Violin at Conservatorio Superior de Vigo
Master's degree in Artificial Intelligence Research Specialty in Machine Learning and Data Science Inteligencia artificial, Master's degree in Artificial Intelligence Research Specialty in Machine Learning and Data Science Inteligencia artificial at Universidad Internacional Menéndez Pelayo