Caio Di Felice Cunha

Leading data-driven research initiatives at the intersection of AI infrastructure, energy systems, and global financial markets. I specialize in transforming complex datasets—from satellite imagery to API streams—into actionable intelligence for industrial projects and institutional investors. - Industrial & Energy Intelligence: Conduct deep-dive analysis into the lifecycle of AI data centers and large-scale energy projects, providing critical insights for industrial development. - Geospatial & Satellite Analysis: Utilize satellite data and governmental records to monitor industrial footprints and infrastructure shifts globally. - AI Frameworks & Automation: Architect and implement AI agents and automated workflows to streamline data engineering pipelines and improve research accuracy. - Financial Market Synthesis: Translate technical industrial data into market-ready intelligence for hedge funds and investors, focusing on the impact of energy and AI trends on portfolios. Technical Integration: Develop and maintain API-driven data pipelines to ensure real-time accuracy in research reporting.

• Achieved a 3% improvement in CapEx analysis within the Mining Sector and Logistic operations by leveraging data analysis tools including Power BI, SQL, Python, R, and machine learning, resulting in more accurate financial forecasts and a streamlined budget allocation process. • Enhanced CRM analysis in the energy sector by 30% through comprehensive data analysis and interpretation, leading to a 10% increase in client retention rates and improved customer satisfaction. • Provided valuable insights into customer behavior and preferences, informing strategic decisions and driving revenue growth by 5%, resulting in increased market share and profitability. • Utilized analytical skills to extract actionable insights from complex data sets, contributing to a 40% optimization of business operations and decision-making processes, leading to improved efficiency and cost savings.

• Spearheaded the development of reports and dashboards in Power BI, resulting in a 30% increase in data visualization efficiency and a 25% reduction in report generation time. Acquired proficiency in Python and SQL programming languages, leading to a 15% improvement in data analysis accuracy and a 20% increase in insights derived from complex datasets within the Finance and Insurance sectors. • Strengthened client relationships by effectively resolving 95% of complex issues within a timely manner, resulting in a 60% increase in client satisfaction scores. Demonstrated leadership in the field, leading to a 35% increase in client referrals and a 25% increase in contract renewals. • Expanded roles and responsibilities beyond initial position, resulting in a 40% increase in project scope and a 30% increase in revenue generation. Proactively learned and applied new skills, leading to a 25% increase in efficiency and a 20% improvement in project completion timelines. Identified and capitalized on client strengths and weaknesses, resulting in a 15% increase in customized service offerings and a 10% improvement in client retention rates.

• Initiated exploration of the Data Analyst field by utilizing ACL and Excel tools within a Construction Company project, resulting in a 20% increase in data accuracy and a 15% reduction in error rates. • Successfully ensured client integrity with government and society by utilizing data analysis techniques to identify and mitigate instances of employees seeking unemployment benefits while employed at the company, resulting in a 95% decrease in fraudulent claims and a 80% improvement in compliance with regulatory standards. • Acquired proficiency in Power BI and coding logic, leading to a 60% improvement in data visualization capabilities and a 80% reduction in data processing time for future projects. Demonstrated a commitment to continuous learning and professional development, resulting in a 20% increase in project efficiency and a 30% increase in project deliverables' quality.

● Explored the Data Analyst field starting with ACL and Excel whilst working in a Construction Company project.

Elaboration, analysis and execution of projects

Process organization/management Execution of sentences

Institucional Responsibility and Community Action

The Data Engineering and Analytics with Co-op program offers a blend of theory and practical applications, preparing students for careers in data engineering and analytics. It covers relational databases, data structures, statistical analysis, machine learning, business analysis, and system design. Students gain experience with data warehousing, visual analysis for business intelligence, and Microsoft SQL Server tools (SSIS, SSAS, SSRS). Key concepts in big data, cloud computing, and data mining are included, along with data security and privacy. Program Benefits The program readies students for data engineering and analytics careers through a curriculum covering databases, statistical analysis, and machine learning. Practical experience with data warehousing and visual analysis equips them for diverse roles. Work Experience The co-op component, totaling 900 hours, allows students to apply their skills in real-world settings with a guaranteed paid work opportunity.