DE

Services

Expertise that turns data into movement.

No hand-offs and no theatre. Senior guidance and practical implementation across the full data lifecycle.

01

Data & AI Strategy

A clear path from fragmented data to dependable AI. I define target architectures, semantic data layers and governance models that connect business priorities with production delivery.

Who it is for: For leadership and technology teams with clear data and AI ambitions but no dependable path from fragmented sources to systems that run in production.

In automotive, this meant shaping the strategy for production machine learning on Azure Databricks for Audi, Porsche and Volkswagen, with governance aligned to GDPR and NIST SP 800-53.

Typical deliverables

Assessment of existing data, systems and the decisions they should supportTarget architecture and semantic data layerPrioritised AI roadmap from first use case to productionModel governance: approval, documentation, monitoring and bias audits

In practice

Automotive AI, from BUS data to decisions
02

Applied Machine Learning

Production-grade models and AI agents for forecasting, classification, behavioral analytics and natural-language data exploration—supported by explainability, evaluation and feedback loops.

Who it is for: For teams that need models working in production—forecasting, classification, behavioral analytics or natural-language data exploration—rather than another prototype.

Past work ranges from retention and churn models that improved marketing return on investment by 28% to an LLM-powered analytics layer for natural-language dataset discovery and PySpark generation.

Typical deliverables

Predictive models with documented evaluationLLM systems and AI agents with review steps and guardrailsExplainability and model reporting for business stakeholdersFeedback loops and A/B tests to measure real impact

In practice

Churn prediction: +28% marketing ROI
03

Data Platforms

Reliable data and ML foundations for high-volume workloads. I build tested pipelines, feature stores and analytical services with Python, Spark, Airflow and Databricks.

Who it is for: For organisations whose data volume or number of sources has outgrown ad-hoc scripts, manual exports and inconsistent reporting.

I have unified CAN, LIN and FlexRay bus data on Azure Databricks, built multi-terabyte Spark platforms and helped migrate ProSiebenSat.1 Media’s analytical warehouse to Apache Spark with a unified KPI reporting layer.

Typical deliverables

Tested data pipelines with Spark, Airflow or DatabricksFeature stores for consistent training and inferenceValidated migration of data, schemas and workloadsMonitoring and observability for data flows

In practice

Warehouse migration to Apache Spark
04

MLOps & Delivery

Operationalise models with reproducible workflows, CI/CD, monitoring and auditable controls. I also mentor teams in clean code, testing and sustainable delivery practices.

Who it is for: For teams that already have models but struggle to deploy, monitor or audit them reliably—or that want to raise their own engineering standards.

At BODYVISER I built secure Airflow and PostgreSQL pipelines for sensitive health data and led technical delivery through the company’s acquisition; in automotive I mentored data scientists in clean code, test-driven development and CI/CD.

Typical deliverables

Reproducible training and deployment workflowsCI/CD for data and machine-learning codeModel monitoring and auditable controlsMentoring in clean code, testing and sustainable delivery

In practice

Secure pipelines for health data

A small, sharp process

Three steps.
Zero theatre.

Every engagement is shaped around fast learning, visible progress and a clean handover.

01

Find the lever

Clarify the decision, bottleneck or repeated task where data can create the most useful change.

02

Prove the path

Build the smallest credible solution, test it against reality and measure whether it deserves to scale.

03

Automate & transfer

Make the winning path reliable, observable and understandable—then put it in your team’s hands.

Working together

How engagements work

Engagements grow with evidence, not with assumptions. You commit to the next step only once the previous one has shown it is worth it.

01

Start small

Most engagements begin with one focused question: where is the lever? The result is a clear view of where data can create useful change—and where it cannot.

02

Proof of value

If the lever is real, the smallest credible solution is built and tested against your actual data and processes, so the decision to scale rests on measured results.

03

Implementation & handover

The proven path becomes a reliable, observable and documented system. Your team is enabled to operate and extend it, so the result does not depend on me.

04

Remote-first, in two languages

I work remote-first with clients across Germany, the DACH region and Europe, and on site by arrangement. Working languages are German and English.

Not sure where to begin?

Bring the messy problem.

Book an initial call