Supply Chain Data Analyst

Freudenberg is a global technology group that strengthens its customers and society long-term through forward-looking innovations. Together with our partners, customers and the world of science, we develop leading-edge technologies, and excellent products, solutions and services for 40 market segments. The Freudenberg Group employs some fifty thousand people in around 60 countries worldwide and generates sales of nearly nine billion euros.
Freudenberg Performance Materials is a leading global supplier of innovative technical textiles for the markets of automotive, building materials, apparel, energy, filter media, healthcare and building interiors, among others. The company generates sales of more than 1 billion euros, has 33 production sites in 14 countries around the world and some five thousand employees. Freudenberg Performance Materials attaches great importance to social and ecological responsibility as the basis for its business success.

• Successfully completed studies in the field of industrial engineering, production, logistics or business administration with focus on manufacturing and supply-chain;
• Preferably experience in production and/or logistics environments gained;
• Strong analytical and problem-solving skills. Data analytics experience in manufacturing or supply chain is sought after, but not a requirement;
• Very good written and spoken English skills;
• Willingness to deepen expertise in quantitative analysis and data science (., large data sets, Alteryx, etc.);
• Knowledge of SAP as well as experience with Power BI preferred;
• Self-starter and intellectually curious with a strong desire to improve business processes through innovation;
• Motivated to gain the full business understanding behind each quantitative analysis;
• Drive to openly communicate and engage in cross-functional discussions.

• Analysis of S&OP, production planning and supply chain processes, in cooperation with central functions and global subsidiaries;
• Consult with internal and external stakeholders to determine how best to apply data analytics and/or statistical learning to support business objectives;
• Continuous development and optimization of analytics tools and supply chain parameters;
• Carrying out root cause analyses as part of optimization projects;
• Identify correlations and independently develop solutions, from end-to-end data collection;
• Apply linear models and algorithms to understand and/or predict events impacting planning and forecasting;
• Define relevant KPIs and develop reporting’s;
• Effectively communicate technical concepts to a non-analytic audience.