Beautiful Soup Experts in Germany

Creation of workflows for generating the data basis for the article import of a web shop: combining data from several sources, analyzing the requirements, designing the process with Jupyter Notebooks and Knime. Also creating code for automation in Python using Polars and Pandas.

Processing the source data, filtering and merging the source files and creating the needed intermediate products, creating the upload files, plausibility checks, quality checks.

Technologies used: PyCharm, Python, Jupyter Notebooks, SQL, Knime. Pandas, Polars

• Managed and maintained daily sales and transaction data, ensuring data accuracy and integrity for operational reporting and analysis.
• Analyzed customer purchasing patterns to support inventory planning and improve product availability.

• Completed a job simulation involving reading web activity logs.
• Supported a client in a cybersecurity breach.
• Answered questions to identify suspicious user activity.

• Built end-to-end pipelines for sensor-driven time-series vibration signals: data cleaning, feature engineering, model selection for unsupervised learning, and validation for size classification tasks.
• Applied statistical feature extraction to characterize dynamic responses.
• Developed Python algorithms for signal processing (FFT, PSD) and classification; compared multiple model architectures with uncertainty-aware evaluation.
• Supervised students in labs and projects (analysis methods, interpretation, reporting); delivered a practical laboratory course and contributed to documentation and presentations.
• Prepared academic outputs: Master’s thesis and conference contribution (SimScience 2025).

• 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)

• Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.

• Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
• Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
• Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS

• Led single-cell and bulk RNA-seq analyses to identify rare airway and immune cell populations
• Designed and deployed multiple interactive R Shiny and Streamlit dashboards for real-time data exploration and communication with clinical partners
• Developed machine learning models for immunotherapy response prediction using single-cell transcriptomics and immune profiling data
• Built reproducible pipelines for single-cell and bulk RNA-seq analyses aligned with clinical and research standards
• Analyzed patient-derived datasets for biological marker identification using reproducible pipelines aligned with research and clinical standards
• Translated computational outputs into clear biological insights for mixed technical and non-technical audiences