At Jabil we strive to make ANYTHING POSSIBLE and EVERYTHING BETTER. We are proud to be a trusted partner for the world's top brands, offering comprehensive engineering, manufacturing, and supply chain solutions. With over 50 years of experience across industries and a vast network of over 100 sites worldwide, Jabil combines global reach with local expertise to deliver both scalable and customized solutions. Our commitment extends beyond business success as we strive to build sustainable processes that minimize environmental impact and foster vibrant and diverse communities around the globe.
Summary of Program Jabil’s Summer Internship Program is set to launch in summer 2025. As an intern, you’ll have the unique opportunity to engage with Jabil’s leadership team and participate in a variety of experiences focused on professional development, networking, and community engagement. You’ll also collaborate with fellow interns in committees, be paired with a mentor, tour Jabil’s facilities, and much more! If you’re eager to be part of a program that will propel your career, apply today and join us on this incredible journey. Join our dynamic AI/ML team at Jabil to gain an understanding of the innovative world of Graph Data Science, Causal AI, and advanced machine learning techniques! This position offers an exciting opportunity to engage with cutting-edge analytics, deep learning models, and supervised learning approaches. You will explore how graph-based analysis, causal inference, and advanced ML models can enhance predictions, identify causal relationships, and uncover hidden insights in manufacturing processes, ultimately contributing to Jabil's operational efficiency and innovation.Learning and Implementation : Gain an understanding of Graph Data Science, Causal AI, and deep learning techniques, and their applications in a manufacturing context. Implement experimental graph algorithms, machine learning models, and causal analysis to uncover insights and drive data-driven decisions.
Experimentation and Analysis : Utilize a Python-based environment to conduct experiments and analyses. Leverage pre-configured graph algorithms, supervised learning models, and automated processes to identify patterns, relationships, and causal drivers within Jabil's extensive data.
Predictive and Causal Analysis : Experiment with graph-based queries, causal inference models, and deep learning approaches to enhance predictive capabilities, identify root causes, and improve decision-making processes.
Collaboration and Sharing : Collaborate with data science teams to integrate supervised learning insights and causal findings into Jabil solutions. Share learnings and contribute to developing new methodologies for operational optimization.
Exposure to advanced techniques : Gain hands-on exposure to a broad set of graph algorithms, causal inference methodologies, and deep learning approaches with practical applications in real-world manufacturing scenarios.
Impactful contributions : Contribute to high-impact projects that enhance operational efficiency, predictive capabilities, and root cause analysis across Jabil's operations.
Experimentation and innovation : Implement supervised learning models and deep learning techniques to analyze relationships, identify trends, and optimize processes in manufacturing environments.
Deliverables : Position papers, best practices/lessons learned, updates to AIML Playbook, Causal AI workflows, deep learning models, supervised learning model experiments, AIML pipelines, and other artifacts that showcase results and innovation.
Major(s): Computer Science, Data Science, Information Systems
Class Year: Masters Student, Recent Graduate
GPA: Min 3.0
Effective Communication
Self-starter – Ability to drive work
Leadership Skills: Agility, Building Trusting Relationships, Decision Making Skills, & Resilience
Availability: Must be available to work in St. Petersburg, Florida from May 19, 2025 – Aug 8, 2025.
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