Nicolas Neudeck

About ​

My name is Nicolas, I am a Data and AI Enthusiast from Munich, Germany.
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During my computer science studies at the Technical University of Munich, I focused on databases, data engineering, and machine learning. Alongside my studies, I worked part-time at companies like E.ON and Amazon, where I got hands-on programming experience. This helped me improve my SQL and Python skills, and learn about cloud services and containerization. Currently, I'm learning Rust, Kubernetes, CI/CD pipelines, and how to test software more effectively. Outside of work, I'm involved in the Munich data science community, enjoy participating in hackathons, and am interested in entrepreneurship.

Curriculum Vitae

  1. AI Engineer at BCG X

    • Developed several GenAI use cases using Python, AWS (Lambda, ECS, RDS), GCP, and LangChain (e.g., platform to compare proposals). Built a RAG Output Quality Framework and enhanced product performance and efficiency.
    • Developed scalable real-time AI applications for CallCenters with AWS (Kinesis, Bedrock, Sagemaker), including real-time audio transcription, data streaming and automated cross-region deployments, handling 2000+ LLM requests/min.

  2. Master Thesis on Federated Learning

    • Conducted Experiments on Fairness in Client Selection Algorithms in Federated Learning Systems

  3. Software Engineer at BCG GAMMA

    • Build an MLOps Pipeline for an internal python library. Comprised of unit and integration tests, test coverage reports, linting, formatting, pre-commit hooks, a CI/CD pipeline, and automatic documentation with Sphinx and GitHub pages
    • Developed a run versioning system for Machine learning applications. In generated run folders, a run's configuration files, log files, and output files are saved. This allows the user to reproduce past runs deterministically

  4. Data Analyst at Amazon Pay

    • Built, updated and deployed ETL workflows that automatically pull and update relevant KYC/Compliance data for the sales team in quick view dashboards. Hereby, demonstrated high level of frugality and reduced weekly time spent on recurring work for sales representatives by 70%
    • Conducted an exploratory churn rate analysis to identify correlations between churn rate and other KPIs, using SQL, Python, Pandas, scikit-learn and Matplotlib

  5. Data Engineer at E.ON

    • Enabled real-time data processing using Kafka, which reduced data loading times from 2 days (batch mode) to a few minutes
    • Established a scalable MLOps framework for the data science department

  6. Data Science at Burda

    • Merged gigabytes of traffic and ad-revenue data to provide unprecedented transparency on revenue per click to management (using Spark, AWS, EMR, Sagemaker)
    • Implemented a personalization algorithm into focus.de which provides a tailored user experience for more than 25M users (DynamoDB, Lambda, Python)