Data Scientist
I build data products end-to-end — from ETL pipelines and dashboards to RAG systems and agentic LLM workflows.
Data science master's student (Web & Data Science, University of Koblenz) with hands-on experience in Python, SQL, and machine learning — plus applied work in GenAI: RAG architectures, embeddings, vector databases, and agentic LLM workflows.
I build data products end-to-end: automated collection, validated ETL pipelines, star-schema analytics, dashboards, and applied ML systems. My GitLab portfolio is one coherent market-intelligence platform — every repo tested, documented, and CI-ready.
Local-first Retrieval-Augmented Generation document Q&A: embeddings, ChromaDB vector search, cross-encoder re-ranking, and cited answers via an offline extractive reader or any OpenAI-compatible LLM.
Multi-source ETL pipeline (REST API + CSV + SQL) that extracts, validates, and transforms data into a star-schema analytical dataset with quality gates, exported as Parquet/CSV for Power BI.
EV market-intelligence dashboard on a clean star-schema model. Every KPI is verified in Python with unit tests before it becomes DAX; ships a Streamlit demo and a Power BI Desktop guide.
Scheduled RPA-style bot that collects signals from RSS feeds, deduplicates them, and produces daily Excel reports — the ingestion layer that feeds the ETL pipeline and the dashboard.
EDA and modeling on weather/sensor data; trained and evaluated recurrent networks (LSTM/GRU) for forecasting with anomaly detection, performance metrics, and clear visualizations.
Recommendation system that suggests music based on the user's facial expressions — image processing, classification, and API integration.
Available for data science, ML, and analytics engineering roles. I'm always happy to talk about data pipelines, RAG systems, or dashboards — feel free to reach out.