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    • Paulo Cysne

    Head of Department - Data Science and Artificial Intelligence

    Paulo Cysne - Head of Data Science and AI
    Head of Department
    Paulo Cysne
    Head of Data Science & AI
    About The Professor

    About Paulo Cysne

    Paulo Cysne is a top data science executive with over a decade of experience in artificial intelligence, machine learning, and advanced analytics. His work involves developing and implementing scaled-up solutions that are data-driven in finance, banking, insurance, and e-commerce.

    He works in the field of machine learning and large language models (LLM) and automation using AI, focused on how complex data can be translated into three main inertial actions of business and strategy.

    Paulo is based in Geneva, Switzerland, and brings a global lens to data-driven transformation and AI governance. He is a member of The Johns Hopkins University community and has a following of over 32,000 practitioners, executives, and researchers in data science and artificial intelligence worldwide.

    paulo.cysne@riuob.us
    admissions@riuob.us
    Core Expertise
    01
    Machine Learning and Artificial Intelligence - Computers guess what happens next by spotting patterns. Machines learn from tons of examples without being told every step. Smart programs handle tasks once thought impossible for automation
    02
    Large Language Models - LangChain, RAG, AI Agents, NLP applications
    03
    Data Science and Analytics - From raw information to clear answers - building paths that connect steps, shaping patterns, revealing what matters
    04
    AI Strategy and Leadership - Teams come together when goals align. Defining where AI goes next happens through clear choices
    05
    Financial and Risk Analytics - Spotting scams, judging loan risks, handling rules automatically
    06
    Machine Learning Operations and System Deployment - Production systems, cloud platforms (AWS, Azure), and scalability
    Academic & Professional Background

    Leadership & Positions

    Paulo combines technical richness with executive-level understanding. His guidance helps organizations implement robust AI ecosystems that are not only high-performing but also interpretable, compliant, and ethical. His approach blends advanced modeling strategies with organizational preparedness to guarantee business impact.

    As Head of the Data Science and AI Department at RIUOB, he focuses on academic rigor, applied research, and industry alignment, building industry-relevant, application-driven curriculum.

    Head of Department · Present
    Head of Department - Data Science & AI
    Roosevelt International University of Baptist - Leading AI education, research, and industry-aligned curriculum development.
    Senior Manager · Geneva
    Senior Manager AI and Data Science
    Independent Consultant, Geneva - Strategic AI implementation and data science leadership.
    Head of Data Science · Former
    Head of Data Science
    UST Global - Leading cross-functional AI projects delivering cost reduction and efficiency improvements.
    Director of Data Analytics
    Director of Data Analytics
    Data Science Startup Geneva - Building analytics capabilities and data-driven solutions.
    Lead Data Scientist
    Lead Data Scientist
    Finance and Risk Analytics - Specializing in fraud detection, credit risk assessment, and automated compliance.
    Roosevelt International University - Department of Data Science
    Roosevelt International UniversityDepartment of Data Science & AI
    10+Years Exp.
    32K+Followers
    4.9Rating
    • Machine learning and predictive modeling
    • Large Language Models (LLM) implementation
    • AI strategy and governance
    • Financial and risk analytics
    • MLOps and system deployment
    Areas of Excellence

    Core Competencies

    Paulo brings interdisciplinary expertise across machine learning, AI strategy, and data science leadership to every lecture, seminar, and doctoral session.

    01
    Machine Learning & Artificial Intelligence
    Computers guess what happens next by spotting patterns. Machines learn from tons of examples without being told every step. Smart programs handle tasks once thought impossible for automation.
    02
    Large Language Models
    LangChain, RAG, AI Agents, and NLP applications - Building sophisticated language models for enterprise use cases.
    03
    Data Science & Analytics
    From raw information to clear answers - building paths that connect steps, shaping patterns, revealing what matters most for business decisions.
    04
    AI Strategy & Leadership
    Teams come together when goals align. Defining where AI goes next happens through clear choices. Moving ideas forward depends on steady growth behind the scenes.
    05
    Financial & Risk Analytics
    Spotting scams, judging loan risks, also handling rules automatically - Comprehensive risk management through AI.
    06
    Machine Learning Operations
    Production systems, cloud platforms (AWS, Azure), and scalability - Ensuring reliable deployment of AI systems at scale.
    Philosophy of Education

    Data is only valuable when it drives intelligent decisions and real-world impact.

    — Paulo Cysne · Head of Department - Data Science & AI, Roosevelt International University of Baptist

    Connect With Paulo Cysne

    Interested in pursuing research in AI, Data Science, or Machine Learning under Paulo's guidance?

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