Detailed Description of the Project

Project Objective

Actively contribute to the development of end‑to‑end analytical solutions for real business use cases in the Supply Chain domain, covering the full lifecycle from data exploration and modeling to production deployment and result consumption, with technical mentorship and guidance from the Global Data Science team.

Challenges for the Student

• Understanding and translating real business problems into data-driven solutions.
• Working with complex, large-scale datasets and ensuring data quality, consistency, and reliability.
• Generating tangible business value.
• Developing analytical solutions that go beyond experimentation, considering scalibility, mantainability, and usability.
• Collaborating effectively within a multidisciplinary, global team and adapting to agile ways of working.
• Applying modern data science practcies, including reproducibilitu, documenttion, and code quality.
• Learning and using an enterprise‑grade data science stack.

Main Responsibilities and Activities to Be Carried Out

• Process, clean, and validate data, ensuring data quality and reliability for analytical and modeling purposes.
• Conduct exploratory data analysis to generate insights and support decision‑making through analytical outputs and visualizations.
• Formulate hypotheses, design analytical approaches, and apply statistical techniques to validate results.
• Perform feature engineering and data preparation to support predictive and prescriptive modeling efforts.
• Develop, evaluate, and iterate on predictive or prescriptive models using machine learning or other advanced AI techniques.
• Contribute to the full lifecycle of Data Science solutions, including documentation, validation, and preparation for production deployment.
• Apply Data Science and Software Engineering best practices, including reproducibility, version control, and clear technical documentation

Specific Requirements / Qualifications

Skills, Competencies, and/or Knowledge Required