21.09.2026 aktualisiert
100 % verfügbar
Data Scientist | Machine Learning Engineer
Warburg, Deutschland
Weltweit
M. Sc. Data Analytics (Universität Hildesheim)Über mich
M.Sc. Data Analytics, Projektingenieur & Consultant für Data & Digital Solutions. Erfahrung in Data Engineering, AI/ML und Softwareentwicklung mit Python, SQL, C++, TypeScript, Docker, PyTorch und Generative AI. Ab sofort verfügbar.
Skills
AI AgentsLLMPythonMachine LearningGenerative AI
Machine Learning
Expertise in classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, data preprocessing and cross validation for building robust predictive models.
AI & Deep Learning
Proficiency with PyTorch, TensorFlow, Hugging Face Transformers, LoRA, DeBERTa, Qwen, Generative AI and Prompt Engineering for developing and fine-tuning advanced deep learning models.
Programming Languages
Strong command of Python, SQL, R, JavaScript, TypeScript.
Cloud & DevOps
Hands-on experience with GIT, Linux, Docker and REST APIs for version control, containerization and building scalable service integrations.
Data Engineering & Analysis
Ability to analyse and validate complex engineering datasets, perform data mapping, dependency analysis and consistency verification across interconnected technical systems.
Software Engineering
Experience designing scalable software solutions, building APIs, integrating external services and developing frontend and backend applications using React, TypeScript and Node.js.
Model Training & Evaluation
Automated model training and evaluation on GPU infrastructure using Linux-based workflows, with evaluation using Accuracy, Precision, Macro F1, Recall and task-level performance metrics.
Technical Documentation
Production of technical documentation, process guidelines, system specifications and knowledge articles for cross-functional teams.
Erfahrung
Berufserfahrung0 - 5 Jahre
Freelancer seit2024
Sprachen
DeutschGrundkenntnisseEnglischverhandlungssicher
Projekthistorie
Analysed and validated complex engineering datasets and system relationships within APIS IQ for FMEA-based risk analysis. Performed data mapping, dependency analysis and consistency verification across interconnected technical systems. Collaborated with engineers and stakeholders to translate technical requirements into structured digital solutions. Automated repetitive workflows using scripting and software solutions, improving efficiency. Supported the development and maintenance of internal web applications and digital engineering tools. Produced technical documentation, process guidelines and system specifications for cross-functional teams. Contributed to data quality assurance by identifying inconsistencies and improved structures engineering information.
Designed an end-to-end machine learning pipeline for hallucination detection in Large Language Models. Integrated and processed over 60,000 unlabeled multi-source training samples. Developed automated pseudo-label generation using multiple transformer-based teacher models. Fine-tuned a DeBERTa-v3-large classifier using Hugging Face Transformers and PyTorch. Applied feature engineering, class balancing, weighted loss optimisation and LoRA fine-tuning. Evaluated models using Accuracy, Precision, Macro F1, Recall and task-level performance metrics. Automated model training and evaluation on GPU infrastructure using Linux-based workflows. Investigated the use of Generative AI for scalable data annotation and model supervision.
Supported enterprise users of Siemens Opcenter Quality. Analysed data-related incidents and investigated root causes. Managed user permissions and enterprise system administration. Collaborated with stakeholders to resolve technical issues. Produced technical documentation and knowledge articles. Improved support workflows through structured incident analysis.