Monte Carlo Simulation Experts in Germany

Statistical Forecasting Algorithm

• Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
• Migration from R/On-premise to Python/Snowflake
• Productionalization on Snowflake in cooperation with data engineers & DevOps

Monitoring Dashboard

• Data engineering for preparation & provisioning of necessary data/resources on Snowflake
• Development & deployment of a Streamlit dashboard in Snowflake

ML-based Probabilistic Forecasting on Vertex AI

• Development of a ML-based probabilistic forecasting algorithm from scratch
• Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI

Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow

• 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

• Simulations and development of hybrid digital twins to enable deep process understanding and to support better decision-making and outcomes in industrial R&D and manufacturing.

Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application

• Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
• Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
• Development of feature store and data synchronization between environments
• Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
• Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
• Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
• Development of Copilot Agent Skills

• Several projects in a police context
• Model and document police procedures/projects with Archimate (as-is/to-be) in the context of P20 (BKA)
• Create software architectures with microservices
• POC development with Springboot/Docker/Kubernetes
• Project size: 10 people
• Enterprise architecture management with Togaf and Archimate
• Backend software development Springboot
• DevOps with Kubernetes
• Implemented using: Java 17/21, Springboot 3, P20 architecture, Togaf, Archimate

• Developing a multi-class classification model to predict plant composition and its spatial and temporal changes using predictors, including satellite images, climate time series, and other environmental data such as land cover, human footprint, bioclimatic, and soil variables.
• Developing recommender systems using contextual bandits for an e-commerce platform.
• Building deep neural network models that predict flood-affected areas.

Predapp is a Sovereign AI and Infrastructure company building AI systems that organisations can own, control, and deploy on their terms, with full data sovereignty. As CTO and investor since 2015, leading the development of the Sovereign AI Platform alongside an advisory practice spanning AI strategy for enterprises, fractional CTO engagements, and technical due diligence for VCs, PE, and family offices.

• Architected the Sovereign AI Platform from zero owning technical vision, infrastructure design, and engineering roadmap; currently deployed at a European hospital, an automotive client in Germany, and two US startups, with active commercial discussions with two leading European hosting providers
• Dubai Health Authority (DHA / Nabidh): Designed and trained AI symptom checker and triage system for national 'Doctor for Every Citizen' initiative under HH Sheikh Mohammed bin Rashid Al Maktoum
• Emirates Airlines: Designed and deployed AI agent for ground personnel accelerating training, improving issue handling, and reducing cost of liquid workforce
• Developed explainable AI triage system piloted at University Hospital Heidelberg and Famagusta Hospital (Cyprus); reduced patient wait times by up to 15% (validation ongoing)
• Built production scheduling engine for US industrial AI startup: RL + Monte Carlo tree search, reducing planning from hours to seconds
• Designed and led the development of semantic search engines using RAG + Knowledge Graphs; developed Agentic Text-to-SQL solution for citizen data scientists
• AI strategy advisory and readiness assessments for enterprise clients, including architecture reviews, maturity assessments, and AI roadmap development

• Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
• Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
• Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
• Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
• Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
• Exported reusable pipelines and trained models with joblib for deployment.

• Lead developer of Prof. Valmed, the first LLM-powered medical device (utilising RAG on a medical corpus of 2.5M+ documents) to receive a CE certification.
• Designed and implemented cloud-native MLOps infrastructure for ENBW’s energy trading analytics division, enabling scalable deployment and monitoring of predictive models.
• Architected end-to-end testing and validation frameworks for AI/ML systems, ensuring quality, compliance, and robustness in critical and regulated applications.
• Conducted professional training on AI testing, EU regulatory frameworks, and quality assurance for production AI systems.

• Built a Databricks ETL pipeline to ingest and clean JSON book metadata from the OpenLibrary API.
• Transformed nested datasets using Python/PySpark and structured them into curated analytical tables.
• Loaded processed datasets into Snowflake to enable SQL based reporting and metadata analysis.

• Responsible for developing and implementing the Risk Management Policy and Plan, ensuring structured risk identification, assessment, and mitigation
• Conducted comprehensive risk assessments on material quantities, procurement schedules, and contractor performance to ensure accurate forecasting and prevent delays
• Developed and maintained a risk register covering technical, financial, safety, quality, and environmental risks with focus on quality assurance and quantity control
• Collaborated with engineering, procurement, and construction teams to identify deviations in material usage and construction standards, reducing rework incidents by 18% and achieving 10% savings on material costs
• Prepared monthly Risk Management Reports highlighting key risks such as supply chain disruptions, cost escalations, and regulatory compliance issues
• Led quantitative risk analyses, including Monte Carlo simulations and sensitivity analyses, to evaluate the impact of uncertainties on project timelines and budgets

• Explored the impact of neural network structure on network-inspired kernels, such as Neural Tangent Kernel (NTK).
• Demonstrated through theoretical analysis and empirical studies that the RKHS of NNGP is a subspace of NTK.
• Explored the connections between these kernels and the Matérn family.

• Provided systemic Agile coaching across multiple teams and program-level contexts in a remote-first environment.
• Redesigned planning, prioritization, delivery, and measurement policies to improve end-to-end flow and decision quality.
• Established explicit WIP limits, service classes, and pull policies to stabilize flow and reduce uncontrolled work intake.
• Introduced flow metrics and probabilistic forecasting to replace deterministic, commitment-based planning.
• Supported rolling-wave planning and hypothesis-driven decision-making at team and program level.
• Built custom scripts, dashboards, and analytical tooling to expose flow behavior, variability, and risk not visible in standard Jira reports.
• Applied a proprietary 7-system organizational performance model to identify constraints and structure targeted improvement experiments.