Essen, North Rhine-Westphalia, Germany
AI, Analytics & Data are core for business! I lead a data science team with one clear mission: „Deliver Business Value to our customers by providing Data Products that leverage the power of Data via Advanced Analytics, Machine Learning and AI.“ We don't just build models—we build Data Products. Products that leverage advanced analytics, machine learning, and AI to solve actual business challenges. Products that deliver measurable value. Whether it's predicting customer churn, optimizing sales operations, or uncovering hidden opportunities in data, we focus on impact over innovation for innovation's sake. Our work lives in production, not in notebooks.
Strategic Leadership | AI Architecture & Business Transformation As Head of AI Insights & Lab, I lead the strategic vision and architectural framework for our organization’s AI-driven transformation. Steering an elite, multi-disciplinary team of Data Scientists, ML Engineers, and MLOps engineers. Together we bridge the gap between high-level business strategy and technical execution. Our mission is to institutionalize Business Value through advanced analytical insights and production-grade Machine Learning. By architecting a robust AI Lab environment, we foster a culture of rapid prototyping and disciplined experimentation. This strategic dual-track approach—balancing stable, scalable ML operations with agile, high-velocity innovation—enables us to de-risk emerging technologies and dramatically accelerate our time-to-market for transformative AI solutions.
"Leadership is not about being in charge. It's about taking care of those in your charge." (Simon Sinek) My job isn't to have all the answers. It's to build a team that does—and create the environment where they thrive. Leading a team of 5 data scientists and machine learning engineers with one clear mission: deliver business value through data products that solve real problems. We don't just build models—we build production-ready solutions. Using Python, Azure ML, Databricks, Snowflake, and Azure Data Lake, my team creates ML-powered products that drive measurable impact across the organization. What we do: - Develop predictive models for customer churn, sales optimization, credit default risk, fraud detection and opportunity identification - Build and run GenAI models for business applications (from RAG bots to OCR solutions) - Conduct advanced analytics that enable data-driven decisions across multiple business units—from sales and toll operations to credit management and corporate strategy - Build scalable data products that move from prototype (AI Lab) to production (AI Delivery) - Partner with external consultants to deliver strategic projects that support business growth How we work: We operate with an agile mindset, focusing on impact over complexity. Our work lives in production systems, not just in notebooks. Every solution we build is designed to create tangible business value for our customers and stakeholders. My background in applied econometrics and empirical research gives me a unique perspective on data science—I know that the best models aren't always the most sophisticated ones, but the ones that answer the right business questions.
At StepStone, I led the development of data-driven products aimed at optimizing the job-candidate matching process for both employers and job seekers. My work focused on leveraging: Machine Learning: Designed and deployed predictive models to assess candidate-job fit, improving recommendation accuracy and hiring efficiency. Natural Language Processing (NLP): Applied state-of-the-art NLP techniques to extract and understand semantic information from job descriptions and CVs. Big Data Technologies: Utilized scalable data pipelines and distributed computing frameworks to process and analyze large-scale recruitment data. Agile Product Development: Collaborated cross-functionally with product managers, engineers, and UX designers in agile teams to iteratively build and refine intelligent matching solutions. These efforts contributed to measurable improvements in user engagement, application quality, and overall hiring success rates.
Conducted empirical research using advanced statistical methods and large-scale microeconomic datasets at the University of Bochum. My research explored how public policies shape firm and household behavior—complex questions that required rigorous quantitative analysis, careful model design, and robust statistical validation. Key activities: Applied econometric modeling and statistical techniques to analyze real-world behavioral patterns Worked extensively with microeconomic datasets, from data cleaning to sophisticated analysis Presented research findings at international economics and statistics conferences Designed and taught university courses in economics Published research in peer-reviewed academic journals This role combined deep analytical work with the ability to communicate complex quantitative findings to diverse audiences—from students to academic peers.
Taught economics courses and launched my research career at the intersection of public policy and behavioral economics. I was responsible for delivering lectures on public economics and microeconomics, developing my ability to break down complex economic concepts for students at various levels. Simultaneously, I began my doctoral research investigating how public goods provision influences firm location and household migration decisions—questions that required combining economic theory with empirical data analysis to understand real-world behavior patterns.