Simon Faltz
Data Scientist | Machine Learning & AI Specialist
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Über
As a data scientist with a strong foundation in machine learning, time-series analysis, and real-time data optimization, I specialize in applying AI-driven solutions to complex challenges in the energy sector. My expertise includes developing advanced predictive models for wind and solar power forecasting, optimizing smart grid operations, and implementing MLOps in production environments. With a Master's degree in Electrical Engineering and Information Technology from the Technical University of Munich, I am passionate about leveraging data to drive innovation and deliver impactful results.
Fluent in German, English, and with basic French skills, I am ready to contribute to dynamic, cross-functional teams.
🧑🎓 Master's from Technical University Munich
💡 Expert in Renewables
🧠 AI Passionate
⚽ Football Fanatic
🏊♂️ Avid Swimmer & Fitness Buff
🚴♂️ Road Cycling Enthusiast
Berufserfahrung
Developer Internship
Technical University Munich (TUM)
Munich, Bavaria, Germany
- Developed an artificial intelligence approach to identify normative behavior in decentralized generators in isolated grids.
- Collaborated on optimizing machine learning algorithms for improved grid stability during islanded operations.
BMW Group
Juni 2021 – Dezember 2021 | 7 Monate
Munich, Bavaria, Germany
- Led data management initiatives to improve AI applications in robotics and logistics.
- Worked on enhancing autonomous material recognition systems and improving the deployment of AI-driven robotics.
Ausbildung
Technical University Munich (TUM)
Master’s Degree in Electrical Engineering and Information Technology
- Specialization: Deep Learning, Renewable Energy Systems (Wind & Photovoltaics), AI
- Master’s Thesis: "Attention-Based Recurrent Neural Networks for Joint Probabilistic Wind Power Forecasting"
Bachelor’s Degree in Electrical Engineering and Information Technology
- Specialization: Machine Learning, Artificial Intelligence, Computer Vision
- Bachelor’s Thesis: "Spatial-Temporal Modeling for Multiple Object Tracking"
