R Experts in Cologne

• Implementation of analytics and automation solutions for the Anti Financial Crime business unit
• Providing the infrastructure, including reusable data models and feature ingestion for production ML and rule based models in the areas of Account Take-Over and Card fraud detection, as well as Customer Risk Assessment
• Tools used: Snowflake, dbt, Looker, AWS, Python, Airflow, Metaflow

• Responsible for the architecture and development of an AI-native SaaS platform for industrial product portfolio management.
• Designed the modern data platform architecture on Azure for scalable analytics and enterprise data integration.
• Built enterprise data ingestion and transformation pipelines across complex industrial system landscapes.
• Developed graph-based representations of product structures and dependencies for analytical reasoning.
• Designed and implemented an agentic AI framework for AI-supported decision workflows.
• Built scalable analytical microservices and integrated reporting through modern BI technologies.
• Coordinated backend, AI, and frontend development across the MYNR platform stack.

• Serve as AI lead for life-science solutions, integrating advanced AI models directly into company workflows and ensuring seamless deployment.
• Design and implement deep learning architectures (PyTorch, Keras) for complex biomedical challenges, including cell segmentation, multimodal omics analysis, and prediction of point clouds.
• Develop and deploy robust LLM-based systems, including RAG architectures and agentic workflows using LangGraph, to facilitate natural-language interaction with complex medical data.
• Lead cross-functional initiatives to apply foundation models and explainable AI (xAI) to clinical and evolutionary algorithms.

• AWS
• Genedata Profiler
• Data Lake
• APIs
• Rstudio
• GitLab
• Python
• R
• Data acquisition, integration, and simulation
• Multiplex immunofluorescence
• Copy-number variation calling
• HLA typing and loss-of-heterozygosity analysis
• RNA expression analysis

• Coordinated feedback and milestones with international co-authors to streamline publication
• Directed data curation and final analysis using Python and Matlab
• Accelerated the final publication timeline by efficiently managing figure preparation and data traceability

• Independently initiated and deployed automated validation workflows using Python, cutting manual processing by 58% and improving efficiency
• Developed a machine learning model for synthetic defect generation, reducing downtime and production costs; deployed locally and via Databricks and Azure AI Factory
• Utilized a small dataset of image data from the production lines and extended this dataset with training on models like cycleGAN and pix2pix
• Built and optimized the Linux-based development environment for training 3D models; maintained reproducibility via GitHub
• Presented technical insights to cross-functional teams (engineers, QA, project managers), ensuring alignment of ML solutions with operational needs

• Designed and led the development of an automated short-video generation system.
• Built a scalable AWS backend using Step Functions, Lambda, S3, ECS Fargate, and DynamoDB.
• Developed video rendering with OpenCV and FFMPEG; ensured maintainable Python code.
• Supervised and mentored a Python developer and trained the client in AI workflows.
• Decreased end-to-end production time from hours to minutes.
• Created a modular, extensible architecture designed to support future AI models.

• Implementation of custom reporting solutions for financial KPIs aligned with specific business requirements

• Development of a machine learning application that achieved a 250% performance improvement

• Data Architect "Production-Oriented Quality Assurance" (03/2024–09/2024):

• Design and implementation of an analytics platform to detect quality deviations in manufacturing

• Build of a scalable data lakehouse architecture on Databricks in combination with SAP ERP data via SAP Datasphere

• Close collaboration with the SAP team to harmonize bill of materials and order data

• Visualization of KPIs to support shopfloor management

• Lead Data Engineer "Sales Performance Monitoring" (08/2023–12/2023):

• Design and implementation of a Databricks-based platform for analyzing sales figures and promotion effects

• Integration of SAP SD data via SAP BW/4HANA

• Use of Azure DevOps to orchestrate ETL jobs and deploy workflows

• Technical Project Lead "Cloud Migration & Data Strategy" (02/2023–05/2023):

• Migration of a heterogeneous data warehouse stack to a modern cloud architecture on Azure with Databricks as the central processing platform

• Development of a governance-compliant data architecture to integrate SAP financial data and non-SAP sources

Technologies: Databricks, Delta Lake, Python, SAP Datasphere, Azure DevOps, PowerBI, SQL

• Build a sophisticated AI copilot to augment the students’ learning experience and provide AI-derived insights to professors.
• Build a multi-chain LLM system adapting to user needs at its own accord with a Weaviate vector DB based RAG system and evaluated it with Ragas.
• Build responsive react frontend, and backend systems handling auth, data management and auxiliary services as a RESTful API.
• Deployed and managed the app to the cloud in a production environment including the CICD via multi-stage deployment.

• Core-IT system assessment for a health insurance company
• Strategy development for a spectroscopy manufacturer
• Project management in the product development of a medical engineering manufacturer
• Process digitalization and development of technical interfaces for a telecommunication provider

• Technical project management, requirements management, and design to guide the data team in developing a predictive maintenance model for DB InfraGO's maintenance planning.
• Statistical modeling and analysis programming with R Studio for fault analysis in preventive maintenance: multivariate modeling using quasi-Poisson, negative binomial, lasso, offset, splines, RandomForest.
• Implementation of various R Shiny dashboards.
• Sparring partner and requirements management for data engineering, data modeling, and ETL pipeline in Tableau Prep.

• Generated and analysed large-scale RNA-seq data (>800 samples) using R, Python, and high-performance computing (HPC/Linux) systems.
• Integrated multi-omics data (genomic, transcriptomic, and phenotypic); applied Bayesian approaches and machine learning to study gene expression variation and inheritance of complex traits.
• Mentored B.Sc. and M.Sc. students in experimental design, programming in R/Python/Bash, biostatistics, data visualisation and scientific presentations.

• Strategy | Concept | Consulting | Project Management | Editing | Copywriting | Reporting | Format Development | Channel Optimization

References · mobile.de | mo:re Conference | mobile.de/more · mediakompetent (journalist, JTI) · Federal Employment Agency · Williams Racing (Formula 1) · Catholic Media Congress 2022

• I look forward to a message or email at [email]