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I am Asad Syed, a Master’s student in Electromobility at FAU, with a strong focus on machine learning, AI, data science, and predictive analytics. I combine a solid mechanical engineering foundation with hands-on experience in ML, 3D visualization, NDT data pipelines, and cross-functional collaboration in Agile environments. I enjoy solving real-world problems at the intersection of hardware and software, thrive in interdisciplinary teams, and am fluent in English and German. I am actively seeking opportunities to apply ML-driven insights to manufacturing, mobility, and automation challenges.…I am Asad Syed, a Master’s student in Electromobility at FAU, with a strong focus on machine learning, AI, data science, and predictive analytics. I combine a solid mechanical engineering foundation with hands-on experience in ML, 3D visualization, NDT data pipelines, and cross-functional collaboration in Agile environments. I enjoy solving real-world problems at the intersection of hardware and software, thrive in interdisciplinary teams, and am fluent in English and German. I am actively seeking opportunities to apply ML-driven insights to manufacturing, mobility, and automation challenges.

Asad Syed

Data Scientist, AI Engineer, AI Developer, +5

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I am Asad Syed, a Master’s student in Electromobility at FAU, with a strong focus on machine learning, AI, data science, and predictive analytics. I combine a solid mechanical engineering foundation with hands-on experience in ML, 3D visualization, NDT data pipelines, and cross-functional collaboration in Agile environments. I enjoy solving real-world problems at the intersection of hardware and software, thrive in interdisciplinary teams, and am fluent in English and German. I am actively seeking opportunities to apply ML-driven insights to manufacturing, mobility, and automation challenges.…I am Asad Syed, a Master’s student in Electromobility at FAU, with a strong focus on machine learning, AI, data science, and predictive analytics. I combine a solid mechanical engineering foundation with hands-on experience in ML, 3D visualization, NDT data pipelines, and cross-functional collaboration in Agile environments. I enjoy solving real-world problems at the intersection of hardware and software, thrive in interdisciplinary teams, and am fluent in English and German. I am actively seeking opportunities to apply ML-driven insights to manufacturing, mobility, and automation challenges.

Available to hire

I am Asad Syed, a Master’s student in Electromobility at FAU, with a strong focus on machine learning, AI, data science, and predictive analytics. I combine a solid mechanical engineering foundation with hands-on experience in ML, 3D visualization, NDT data pipelines, and cross-functional collaboration in Agile environments.
I enjoy solving real-world problems at the intersection of hardware and software, thrive in interdisciplinary teams, and am fluent in English and German. I am actively seeking opportunities to apply ML-driven insights to manufacturing, mobility, and automation challenges.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

German

Advanced

Work Experience

Master Thesis Student at DSS, Schaeffler & FAPS-Chair, FAU

August 31, 2025 - October 24, 2025

Developed a 3D visualization tool integrating 2D ultrasonic scans with part geometry using Python and Azure Machine Learning Studio, enabling predictive defect detection. Performed signal quality analysis and feature extraction to improve predictive anomaly detection models.

Working Student in Data Science and Machine Learning at DSS, Schaeffler

January 31, 2025 - October 24, 2025

Automated ingestion and transformation of high-frequency Non-Destructive Testing sensor data into ML-ready Parquet datasets in Azure Synapse for predictive maintenance models. Integrated rail-bearing sensor data from the Conditional Analyzer into Digital Twin pipeline infrastructure to support fault prediction and maintenance analytics. Delivered solutions in an Agile cross-functional team.

Project Thesis Student at FAPS-Chair, FAU

November 30, 2024 - October 24, 2025

Modeled ISO/TS 15066–compliant robotic safety system with system behavior modeling and failure prediction, increasing operational safety by 35%. Enabled robot to follow verbal commands using speech recognition and OpenCV, identifying and handing objects to the user.

Data Science Intern at IFT, TU Wien

April 30, 2024 - October 24, 2025

Processed machine tool power data, built KPI dashboards in Python to identify high-load patterns, enabling predictive optimization and reducing energy costs by 27%. Developed structured Asset Administration Shell for data traceability to support ML-ready manufacturing datasets.

Systems Engineer at Infosys Ltd.

October 31, 2022 - October 24, 2025

Streamlined data pipelines with backend teams and supported reporting and visualizations. Delivered client reports and presentations, helping improve system reliability and boost client satisfaction.

Master Thesis Student at DSS, Schaeffler & FAPS-Chair, FAU

August 1, 2025 - October 24, 2025

Working Student in Data Science and Machine Learning at DSS, Schaeffler

January 1, 2025 - October 24, 2025

Automated ingestion and transformation of high-frequency Non-Destructive Testing sensor data into Machine Learning ready Parquet datasets in Azure Synapse for predictive maintenance models. Integrated rail-bearing sensor data from the Conditional Analyzer into Digital Twin pipeline infrastructure to support fault prediction and maintenance analytics. Delivered solutions in an Agile cross-functional team.

Project Thesis Student at FAPS-Chair, FAU

November 1, 2024 - October 24, 2025

Data Science Intern at IFT, TU Wien

April 1, 2024 - October 24, 2025

Systems Engineer at Infosys Ltd.

October 1, 2022 - October 24, 2025

Streamlined data pipelines with backend teams and supported reporting and visualizations. Delivered client reports and presentations, helping improve system reliability and boost client satisfaction.