Data Scientist & AI Engineer (PhD in Physics) with 6 years of experience developing high-performance Machine Learning (ML) models for complex systems. Expert in pipeline optimization, Automated Machine Learning (AutoML), and computational cost reduction. Proven track record in cloud environments (AWS), open-source contribution (CI/CD), and Web3 architectures for decentralized data traceability.
CONICET / National University of C ́ordobaC ́ordoba, Argentina –Engineered multi-objective predictive models using adaptive algorithms, reducing inference time for complex physics models to∼8ms while maintaining high fidelity. –Implemented end-to-end AutoML pipelines using Data Robot (evaluating 80+ algorithms) and Bayesian hyperparameter optimization with Optuna. –Lead Architect of the open-source libraryscikit-reducedmodel (scikit-learn compatible). Integrated CI/CD workflows (Git Hub Actions),tox, and achieved93% unit testing coverage. –Designed an image compression codec (AQMP) that outperformed JPEG efficiency with 4x higher compression rates in specific domain scenarios. –Peer-reviewed author of 4 international publications on predictive modeling and applied blockchain. –Keynote speaker at international scientific conferences.
Innova CONICET – Amazon Web Services (AWS) C ́ordoba, Argentina – Developed Blockchain Tracer in Python for immutable ML model traceability. Designed hybrid storage (on-chain/off-chain via IPFS) compliant with industrial Model Cards standards. – Architected Solidity Smart Contracts (EVM) for Green Hydrogen traceability, including a Proof of Concept (PoC) for its tokenization (ERC-20). – Awarded 2nd Place at the Algorand Mega-Ace Hackathon for developing a blockchain-based green hydrogen supply chain traceability solution.
PhD in Physics
CONICETC ́ordoba, Arg.
B.S. in Physics
Postgraduate Diploma in Data Science