Principal Component Analysis Expert in Germany

Technical environment: BDC

• Design of BDC project: role design, space design, authorization concept, naming convention
• Template development of database artifacts for master and transactional data

• Development High-Performance ad distribution via auction
• Holistic (multi-campaign & multi-channel) advertisement placement optimization
• Algorithmic optimization for NP-Hard/NP-e
• Multiple Knapsack Problem with constraints
• Online estimation of parameters in stochastic environments

Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker

• Management of the media agency
• Media strategy for various campaigns
• Campaign management
• Development of a social media concept
• Review of reports and PCAs

• Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
• Enhance chatbots with RAG, in-context learning
• Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
• Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
• High-throughput serving with vLLM
• Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
• Generate and filter synthetic data, clustering
• Detect hallucinations
• Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
• Visualization of experiments (matplotlib)

• Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
• Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
• Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
• Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
• Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
• Exported reusable pipelines and trained models with joblib for deployment.

• CO greenfield implementation on S/4 HANA (margin analysis, contribution margin accounting according to two parallel standards)
• Concept for data migration for the integration of a company code
• Set up a parallel contribution margin accounting solution via extension ledger with alternative valuations
• Definition and implementation of derivation strategies
• End-user and key user training

• Gathered tire images and their feature descriptions.
• Cleaned dataset of image metadata using pandas.
• Stored image feature embeddings in Chroma vector db.
• Used image augmentations to increase dataset size.
• Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
• Used PyTorch to train and test different neural networks.
• Validated model using custom accuracy metric based on similarity search in ChromaDB.
• Visualized accuracy predictions using matplotlib.
• Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
• Created custom Docker image in Amazon Elastic Container Registry for machine learning script.

• Applied AI-driven CADD methodologies for biosynthetic pathway optimization and molecule screening.
• Integrated synthetic biology with computational chemistry workflows for rapid in-silico experimentation.
• Automated ML pipelines using Python, PyTorch, and Scikit-learn on Linux, improving model testing and reproducibility.

• Lead engineer for the MatchMaker Toolbox (KNIME): Index Builder, Approximate Matcher, Character Mapper, license nodes
• Designed M|ARS (MatchMaker Agentic Retrieval System) — hybrid retrieval combining deterministic search + LLM tooling
• Developed internal RAG and search prototypes (MatchMaker + vector search + LLM)

• Developing an AI-based corrosion detection system for maritime infrastructure as part of the CHAI Research Project.
• Built a binary image classification model to detect corrosion using a dataset of 5,000+ metal surface images.
• Automated data labeling from segmentation masks and designed bounding box generation workflows for individual corrosion areas.
• Conducted data preprocessing, data augmentation, and model evaluation (accuracy, precision, recall, F1-score).
• Collaborated with the research team to integrate computer-vision workflows for corrosion monitoring and dataset enhancement.

• Mechanical engineering
• Post-merger integration
• Preparation for S4/HANA implementation
• VIM implementation

• Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
• Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
• Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
• Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
• Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)

• Implementation of the valuated customer stock including the FI account-determination logic at an international machine manufacturing company
• Analysis, re-design and agile implementation of an overall CO concept with SAP S/4HANA at an international automotive manufacturer
• End-to-end full scope CO process design (incl. integration into SD, MM, PP, FI, PS) and implementation at an international machine and plant manufacturer
• Re-organization and implementation of Profit Center Accounting (PCA) in SAP ERP at an international company in the mechanical & plant engineering industry

• Built a transparent classifier using rational functions with over 90% accuracy on MNIST
• Tech. Stack: Python, NumPy, SciPy, Scikit-Learn, CuPy, CUDA, SageMath, Gurobi