Miguel De Frutos

Desarrollo de arquitecturas de datos e infraestructura Cloud para proyectos de Inteligencia Artificial dirigidos a PYMES y startups. Diseño y despliegue de pipelines de datos (ETL/ELT) y entornos en la nube (AWS) para operacionalizar los modelos de Machine Learning y NLP desarrollados por el equipo de ciencia de datos. Contenerización de aplicaciones y software a medida mediante Docker, y automatización de despliegues (CI/CD) utilizando GitLab. Gestión de bases de datos relacionales y NoSQL (PostgreSQL,

- Centralization and structuring of data from 15 optical stores through integration with the GIO software using ODATA, optimizing access and data analysis at a centralized level. - 40% reduction in data management and report generation time, improving operational efficiency and freeing up resources for strategic tasks. - Creation of key reports that have supported 80% of strategic decisions at the executive level, enabling faster and more accurate decision-making. - Preparation for the implementation of

- Designed and developed internal full-stack applications using React and Django, containerizing services with Docker and automating deployment pipelines via GitLab on AWS. - Built a cloud-native data infrastructure to centralize performance, workload, and utilization metrics across the ML Engineering team, enabling scalable and data-driven resource management. - Analyzed benchmark and performance data from AI models including Llama, DeepSeek, and Mistral, delivering visual reports that informed both te

- Led the Business Intelligence strategy, overseeing end-to-end data solutions and fostering collaboration with Data Science and Engineering teams to deliver analytics projects 30% faster. - Developed data governance policies to ensure accuracy and consistency across business units, reducing data-related incidents by 40%. - Spearheaded the adoption of Microsoft Copilot and self-service analytics (Power BI/Qlik Sense), training 200+ employees and reducing ad-hoc reporting by 70%.

- Architected scalable data pipelines on AWS (S3, Athena, RDS), collaborating in the design of cloud infrastructure that processed datasets 50% faster. - Engineered automated ETL processes integrating REST APIs (ERP, CRM) into Qlik Sense, reducing manual data integration efforts by 35%. - Implemented web scraping workflows (Selenium, BeautifulSoup) to enrich business datasets, identifying new market opportunities.

- Mentored 20+ students in core Data Science technologies (Python, Pandas, SQL, Scikit-Learn), assisting in debugging complex code and optimizing algorithms for performance. - Facilitated technical workshops on Machine Learning concepts and data visualization, bridging the gap between theoretical concepts and practical business applications.

- Collection and analysis of clients and staff data to identify patterns and trends, leading to a 20% improvement in the effectiveness of marketing strategies. - Implement a lead tracking system, resulting in a 15% improvement in the conversion rate of leads to actual customers. - Development and analysis of surveys and questionnaires to obtain valuable feedback from students and staff, which allowed us to identify areas of opportunity and improve the services offered by 30%. - Creation and presentation

Applied Technologies: Python: numpy, pandas, matplotlib, seaborn, plotly, scikit-learn, pyspark, Flask. Data acquisition techniques: APIs, web scraping, RegEx. Business intelligence tools: Tableau, PowerBI, Streamlit. Machine Learning: classification, regression, clustering, neural networks, SVM.