Marco Schmid
Lead Data Scientist @Ai-Predict Gmbh
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WORK HISTORY
Lead Data Scientist @Ai-Predict Gmbh
Regularly explained model assumptions, limitations, and validation results to non-technical stakeholders.Lead Data Scientist – Client Solutions & ML Innovation• Led the technical acquisition and delivery of the platform’s first enterprise client, taking end-to-end responsibility from problem formulation to production deployment.• Designed and validated hybrid machine learning models for coatings applications, improving predictive performance and robustness compared to established software approaches under real-world constraints.AI Project Lead – Client Acquisition & Materials Innovation• Led a strategic client project (3 FTEs), coordinating data science, domain experts, and software development to deliver production-ready ML solutions for coatings R&D.• Developed and deployed machine learning models accelerating materials development cycles while maintaining model interpretability and validation standards.
EDUCATION
Julius-Maximilians-Universität Würzburg
Master of Science (M.Sc.)
Université Paris-Saclay
Doctor of Philosophy (PhD)
FernUniversität in Hagen
Academic student
Julius-Maximilians-Universität Würzburg
Bachelor of Science (B.Sc.)
Ecole polytechnique
Doctor of Philosophy (PhD)
ABOUT MARCO SCHMID
I am a senior data scientist with a PhD in biophysics and strong experience building and deploying data-driven solutions in startup and industrial environments. I combine deep scientific training with applied machine learning to turn complex physical and chemical data into reliable, production-ready insights, with a clear trajectory toward technical leadership.My work focuses on developing explainable, validated models for measurement-intensive systems, where data quality, uncertainty, and edge cases matter as much as raw performance. I regularly bridge experimental science, software development, and business-facing stakeholders, working fluently in English, German, and French, with advanced Spanish and basic Chinese.Key contributions include: Hybrid modeling approaches combining physical insight with machine learning Quantitative signal extraction pipelines for complex spectroscopic and chemical datasets Novel baseline correction and advanced filtering methods improving data reliability High-speed peak detection algorithms deployed in production-grade analytical instruments Robust testing and validation routines accelerating development while ensuring model integrityCurrent focus areas: AI-supported tools for laboratory method development and decision support Modeling liquid–solid–gas interactions in complex chemical systems Pattern recognition and anomaly detection under sparse and edge-case conditions Reliable UV/Vis detector data processing for industrial instrumentation International collaboration between research institutes and industry partnersLooking ahead, I am interested in senior or lead-level roles where scientific rigor, explainable AI, and scalable data solutions are essential — including positions at the intersection of advanced technology, industrial applications, and intellectual property.
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