Daniel Ibáñez is a Senior Data Scientist and AI engineer based in Peru with eight years of experience building production-grade machine learning solutions across industry and academia. He currently works at Globant while mentoring AI talent at Veritas AI and has research experience from the University of Oxford focused on clinical magnetic resonance applications. His background spans insurance analytics, industrial data science, and teaching deep learning at UTEC, reflecting an ability to translate research into deployable systems. Trained through MIT programs in statistics and data science and grounded in a computer science degree from PUCP, he blends rigorous statistical foundations with practical engineering. Colleagues know him as someone who moves projects from prototype to production and invests time mentoring the next generation of AI practitioners. An understated but distinctive strength is his cross-sector fluency—academic rigor, enterprise delivery, and hands-on mentorship—enabling impact on both models and teams.
8 years of coding experience
4 years of employment as a software developer
Computer Science, Computer Science at Pontifical Catholic University of Peru
MicroMasters Program in Statistics and Data Science, MicroMasters Program in Statistics and Data Science at MITx Courses
Advanced Program in Data Science & Global Skills, Advanced Program in Data Science & Global Skills at MIT IDSS & BREIT
We strive to transfer realistic features from photos of real faces to avatar styles. An end-to-end ML application using PyTorch, Weights & Biases, Flask API, Docker and ReactJS.
Contributions:41 commits, 47 pushes, 10 branches in 1 year 8 months
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