Georgios Papadomichelakis

🌟 TPCE is responsible for handling ≈93% of the European volume, with over a thousand employees dedicated to maintaining the smooth flow of automotive parts. TPCE is the central hub of a vast logistics network spanning 13 depots and 8 independent warehouses. We manage logistics for over 250,000 part-numbers, serving customers across 47 countries worldwide. 1️⃣ Data Analysis: Leveraging SQL, Python, and Power BI to extract valuable insights from extensive datasets. 2️⃣ Report Generation: Providing crucial reports to inform data-driven decision-making processes. 3️⃣ Process Automation: Developing automation solutions to streamline operations and improve efficiency. 4️⃣ Software Development: Create software that enhance our logistics and supply chain management. 5️⃣ Data Cleaning and Preparation: Ensuring data quality by cleaning, transforming, and structuring them for analysis. 6️⃣ Data Visualization: Creating meaningful and interactive data visualizations 7️⃣ Stakeholder Communication: Collaborating with business stakeholders to understand their data needs and translating technical findings into actionable insights & help stakeholders understand complex data patterns, bridging technical and business teams. 8️⃣ Data Strategy Development: Contributing to the development of data strategies that align with the organization's goals and objectives. 9️⃣ Business Performance Analysis: Evaluating (KPIs) to assess the overall health and performance of the organization. 🔟 Data Storytelling: Effectively communicating insights through data storytelling, making data-driven recommendations more compelling to non-technical stakeholders.

R&D - Powertrain Division - Model Based Design - Virtual Routing Tool Development 1️⃣ Understanding the real-world usage of future electrified vehicles such as Fuel Cell (FCEV), Battery (BEV) or Hybrid (HEV) electric vehicle, for optimal design and sizing of their powertrain. 2️⃣ Cleaning & enhancing data - ensuring data quality 3️⃣ Analyse driving data of real vehicles in the market 4️⃣ Apply feature Engineering to create new features or transformations of existing features from raw data, in order to improve the performance of physical & machine learning models 5️⃣ Propose improvements to the virtual routing tool 6️⃣ Apply the virtual routing tool to answer specific development requirements 7️⃣ Apply mathematics/physics/statistics to build models (hybrid mix of physical and data-driven/ML models), validate them and draw conclusions 8️⃣ Propose further development needs