IT Data Science Internship

12 ago - Roma
Procter & Gamble

Innovation is the driving force behind everything we do at P&G.; Across the world, you will find thousands of scientists, engineers and developers working in manufacturing plants, innovation centers and distribution facilities.
You will be a member of the manufacturing team driving end-to-end data and analytical solutions supporting the development, learning, and operational insights for our critical new engineering led initiatives in equipment, process, packaging, customization and supply chain. You will apply Data Science and Analytics techniques to support our Fabric and Home Care business and fuel our value creation pipeline by:
- Partnering with our global teams to identify needs to help our production floor, facilitate our work processes, or improve our Business in general
- Being responsible for the end-to-end development on of sustainable workflow and technologies to support these solutions
- Guiding and supporting the systematic use of analytics in Product Supply organization, in collaboration with domain experts and information systems specialists





This will be the ideal place to work on groundbreaking improvements at our manufacturing sites to apply Data Science and analytics technologies to support our Fabric and Home Care Business and fuel our value creation via digital solutions. Our aim is to ignite your potential and equip you to improve the capability, safety, security, and efficiency of all our systems while reducing cost and boosting sustainability.
As Data Science Intern you will be developing innovative digital solutions that will support
Automation systems, control information networks, vision and robotic systems which deliver outstanding and highly efficient production systems. Engineers in this field leverage their passion for mechatronics and process dynamic simulation tools to create industrial solutions to enable efficient production.
What tasks and responsibilities can you expect?
As Data Science Intern, you will:
- Develop analysis of equ

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