R Shiny Experts in Germany

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

Development of AI-based and classical models for analyzing medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, and referral profile analyses. Analyses in the area of Real World Evidence.

• Gathering customer requirements
• Planning the subproject
• Designing and defining KPIs
• Designing and developing models and visualizations of the results using customer dashboards
• Developing and implementing DWH adjustments
• Deriving recommendations for action

Methods, technologies: Simulation, Artificial Intelligence, Python, R, SQL, Microsoft Power BI, Amazon Web Services, Elasticsearch, PostgreSQL, Databricks, Multivariate Statistics

• Supported the International Web Presence project of the Federal Employment Agency (IntWeb) in quality management, taking on responsibility for the quality of processes and project deliverables while adhering to BA standards. The project's main goals are to give professionals abroad a quick overview of their chances to move to Germany and to enable them to take the necessary steps in a consistently digital way.

• Set the fundamental guidelines using the QA handbook

• Summarized test results in QA reports for PLA

• Analyzed project outcomes for improvement opportunities

• Quality management of requirements analysis (especially processes, methods and tools)

• Ensured compliance with SERA guidelines

• Created a cross-project test concept

• Agreed on sprint completion reports

• Conducted formal reviews of deliverables according to guidelines and/or project plan

• Acted as contact person for internal audit and external audits by auditors or the Federal Audit Office (BRH)

• Technologies: JIRA, Confluence, MS Office, GitLab, Kubernetes

• Research multimodal AI systems integrating image, text, and structured data.
• Build end-to-end AI pipelines for data processing, model training, and evaluation.
• Develop and test computer vision and image recognition solutions using deep learning.

• 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.

• Developed a retrieval-augmented generation (RAG) application using LangChain and LangGraph for corporate document parsing, delivered as an installable Electron desktop application with local AI models via Ollama.
• Currently providing ongoing AI feature support for the Loan Pricing Tool at Hamburg Commercial Bank, with a commitment of three days per month.
• Architected Kubernetes-native solutions, including Helm chart configuration and Azure DevOps pipeline integration.

• Implementation of business transaction layouts for the online banking UI in compliance with BITV (accessibility)
• Technologies: JavaScript, jQuery, LESS

• Development & deployment of an interactive dashboard that allows users to select datasets for visual analysis.

• The application supports filters, AI-based interpretations, and a chatbot for user interactions.

• shiny, openai, mirai, plotly, mapgl, duckdb, AWS, ShinyProxy, Docker Swarm

• Developed a React-based research platform with interactive 3D/AR product visualization workflows.
• Implemented interaction tracking and usage analytics to measure user behavior and feature engagement within a research platform.
• Deployed the research platform and integrated a R Shiny analytical dashboard, enabling researchers to interactively explore meta-analysis results within a unified platform.

• 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.

• Implementation and mapping of regulatory requirements (CSRD, ESRS) in Shiny dashboards and backend functionalities
• Design and development of data-driven modules in Shiny including ESG metrics and reporting templates
• Integration of data sources (SQL, SAP HANA, APIs, CSV/Excel) using DBI, odbc, httr and readr
• Creation of interactive visualizations with plotly, highcharter and DT (DataTables)

• Training Machine Learning models for different purposes.
• Programming with languages such as R and Python, depending on the project requirements.
• Develop, test, and maintain R shiny applications for semiconductor wafer fav production.
• Documentation of the code, project requirements, design decisions, and development processes.
• Perform integration testing, and debug to ensure software functions correctly.
• Coding, debugging, and ensuring quality standards for smooth operations.
• Deployment of Git and GitHub to manage changes and collaborate efficiently with fellow developers.
• Deployment of Docker and Jenkins for applications containerization.
• Staying updated on the latest tools, technologies, and best practices in software development.
• Providing back-up in MES (Manufacturing execution system).
• Getting involved in project management like task priority and project time lines.
• Maintaining and optimizing deployed Apps.

• Developed and maintained data analysis pipelines for plant synthetic genomics with a focus on chloroplast engineering.
• Developed a pipeline to detect true NUPTs (nuclear plastid DNA insertions) in plant genomes using long-read sequencing data.

• 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.