Probabilistic forecasting, agentic systems, and ten years of analytics turned into production ML.
Data Scientist and AI Engineer with ten years of analytical experience across finance, telecom, and gaming. I build production forecasting and applied AI systems, currently shipping ML revenue prediction at InnoGames with Prophet, PyMC, and TimesFM on GCP, while completing my M.Sc. at Leuphana University on a Deutschlandstipendium. My thesis develops an open-source agentic forecasting architecture combining Time Series Foundation model, PyMC, and dual-process LLM reasoning over an episodic memory of past prediction cycles, deployable entirely on EU infrastructure.
An agentic forecasting architecture for game revenue.
My M.Sc. thesis explores how two complementary agents (a fast forecaster and a slower auditor) coordinate over an episodic memory of past prediction cycles to improve probabilistic three-month revenue forecasts at InnoGames.
Work in progress through September 2026. Further details once the thesis is submitted.
Ten years in analytics, from telecom M&A research to production forecasting.
That path shaped my technical taste. I favor methods that are interpretable, auditable, and defensible to non-technical stakeholders: Bayesian inference, structured memory, instrumented forecasts. I build systems that hold up when the data is messier than the textbook suggested.
Currently completing my M.Sc. in Management & Data Science at Leuphana University as a Deutschlandstipendium scholar (GPA 1.8), while working at InnoGames on production ML forecasting. Open to full-time roles in Germany from December 2026.
Selected Projects: Bayesian inference, knowledge graphs, agentic AI
What I work on
I build production forecasting and decision-support systems for teams that need defensible predictions, not just performant models. My current focus is hierarchical Bayesian inference, applied LLM and agentic systems for analyst workflows, and time-series foundation models running on EU-deployable infrastructure. The projects below span all three: knowledge-graph question answering, hierarchical Bayesian customer lifetime value, and a multi-agent forecasting architecture.
Knowledge Graph Question Answering
Team project: SPARQL-based question answering over structured knowledge graphs, enabling natural language queries over RDF knowledge bases.
KVG ML Route Modelling
Team project with a regional transit partner: applied ML to model and predict transportation routes, combining geospatial data with predictive modelling.
Neo4j Graph Analysis of Artist Influence Networks
Graph database solution using Neo4j and Cypher to analyse artists' influence and cluster musical lineages.
Hierarchical Bayesian Pareto/NBD: Replication of Abe (2009)
Implemented and validated the hierarchical Bayesian Pareto/NBD model from Abe (2009) on the canonical CDNOW dataset.
ChefTreff AI Hackathon: Product Detection
Built in 24 hours: a computer vision pipeline for identifying broken or damaged objects from product imagery.
Writing on probabilistic ML and agentic systems
Looking for a data scientist or AI engineer in Germany, Hamburg or Munich, from December 2026?
I bring the most value in forecasting automation, scalable analytics pipelines, and agentic system design, with a focus on open-source, EU-deployable architectures.
My work fits six kinds of teams well:
- Insurance and reinsurance analytics. Underwriting segmentation at Absolute Insurance plus current Bayesian forecasting research, applicable to Munich's insurance cluster and beyond.
- Gaming and consumer products. Currently building ML revenue forecasting and analytics infrastructure at InnoGames in Hamburg, including Microsoft Fabric migration and Power BI dashboards.
- AI-native startups and applied AI labs working on LLM systems, agentic architectures, and retrieval. My thesis builds a stateful multi-agent forecasting system with LangGraph and federated graph memory.
- Telecom and network analytics. Eight years at MTS covering market analysis and M&A across Indian, Serbian, Slovenian, and Moldovan markets, relevant to Munich's operator headquarters.
- Global tech firms and platform teams in Munich and Hamburg where forecasting, applied ML, and production analytics scale across products.
- Startups and scale-ups shipping production ML and agentic systems as core product, not pilots.
I'm comfortable in English C1, German B2, and Russian native. The last decade has been about shipping analytics for international teams across three industries.