*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.- 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.- 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.*specific requirements / qualifications*skills, competencies, and/or knowledge required*- strong analytical and problem-solving skills, with the ability to break down complex business problems into structured analytical tasks.proficiency in python, with the ability to write clean, reusable, and well-structured code for data analysis and modeling.- solid knowledge of sql.- ability to communicate insights effectively through data storytelling, using clear analysis and visual outputs.- strong collaboration skills and the ability to work effectively within a team-oriented, agile environment.- high level of attention to detail and a structured approach to problem solving.- english proficiency sufficient for technical and professional communication in a global team.- familiarity with version control tools (e.g., git)*nice to have*- familiarity with snowflake or other analytical data warehouses.- knowledge or practical experience in simulation techniques (e.g., discrete-event simulation, monte carlo simulation).- knowledge or practical experience in optimization methods, such as linear, integer, or nonlinear optimization, heuristics, or metaheuristics.- familiarity with optimization or simulation libraries/tools (e.g., or-tools, pulp, gurobi, cplex, simpy, or similar).- experience with data visualization tools such as power bi or tableau.- exposure to ai models beyond academic coursework.- experience working with enterprise-grade data platforms or cloud-based analytical environments.(azure, aws, etc.)