I am Michael Princ – a Data Scientist and ML Engineer from Prague. I specialize in data analysis, machine learning, and helping people understand and use artificial intelligence in practice.
What Sets Me Apart
My unique combination of academic background in economics and practical experience in data science allows me to approach problems from both perspectives. I understand both the technical aspects of machine learning and the business context in which models are deployed.
Unlike purely technical specialists, I can:
- Connect data with business – not just build a model, but ensure it delivers real value
- Communicate clearly – thanks to 9 years of teaching at a university, I can explain complex concepts simply
- Understand the broader context – experience in public sector, banking, and academia gives me a unique perspective
My Story
My path to Data Science was not straightforward – and that is precisely my advantage. I started as an economist at Charles University, where I spent nearly a decade in research and teaching. Working with economic data and statistical models naturally led me to the question: "What if we could extract even more from data?"
I found the answer in Machine Learning and artificial intelligence. The transition from classical econometrics to modern Data Science opened entirely new possibilities – from predictive modeling and process automation to working with big data on platforms like Databricks.
My experience at the Office of the Government of the Czech Republic, the central executive office supporting government strategy work and the National Economic Council, showed me how important data work is for strategic decision-making at the highest level. And working in banking (Equa bank) and BI consulting (Dolphin Consulting) gave me practical insight into how companies actually work with data.
Today, I focus on Data Science and machine learning in the banking sector, lead practical AI workshops, and help people understand the possibilities of modern technologies.
Work Experience
Data Scientist & ML Engineer – Raiffeisenbank a.s. (2019 – present)
Currently collaborating as a Data Scientist and ML Engineer with Raiffeisenbank a.s. In this role, I contribute to numerous data and ML projects across various banking divisions. Raiffeisenbank a.s. is part of RBI Group and one of the largest banks in the Czech Republic.
Selected areas of collaboration:
- Unified ML Pipeline: design and implementation of an end-to-end pipeline on the Databricks platform, which today manages hundreds of production ML models
- Propensity models, CRM campaign optimization, and NBO (Next Best Offer)
- Dynamic loan pricing and analyses for credit limit increases
- Deployment of mortgage rollover models and related analytical support
- Processing millions of client records (Spark, PySpark, Oracle DBMS)
- Business analyses, data transfers, and automation (PL/SQL, Power BI, MS Excel)
- Implementation of internal ML platform APEX (built on top of Databricks)
- Automation on Hadoop, Hortonworks, and Databricks platforms
- Collaboration on AI implementation within the bank
- Implementation of MCP servers for AI agents in a corporate environment
- Workshops and presentations on AI usage for various departments (CRM, IT, product, DWH)
- Integration of Databricks, MS SharePoint, Oracle DBMS platforms
Freelance Data Scientist (2019 – present)
As an independent consultant, I help companies with data analysis, ML model development, and implementation of data solutions. I focus on end-to-end projects – from understanding the business problem to deploying a working solution.
Selected results:
- Improved acquisition rates in campaign deployment by 15%
- Increased lift statistics after updating production models by 10% to 60%
- Designed a Unified ML pipeline that reduced model deployment time from 10 to 2 days
- Workshops and presentations on AI usage for bank stakeholders and the public
Economist and Assistant Professor – Charles University (2009 – 2018)
Nearly a decade at a prestigious Czech university, combining research with teaching. This experience gave me the ability to explain complex concepts clearly – a skill I use today in workshops and training sessions.
National Economic Council – Office of the Government of the Czech Republic (2010 – 2013)
Collaboration on economic analyses for the highest levels of public administration. Working with macroeconomic data and preparing materials for strategic decisions.
Equa bank a.s. – Liability Product Analyst (2018)
Working in the product team. Analytical support for decision-making in retail banking. Collaboration with the credit department from the position of product division analyst.
Dolphin Consulting – BI Consultant (2019)
Business Intelligence consulting, design of reporting solutions and data pipelines.
Technical Focus
Core stack for production ML and data pipelines
- Python, SQL, PySpark, Databricks, MLflow, Optuna – the stack I use to design, experiment with, and operate production models and data pipelines
- Predictive modeling, scoring, CRM analytics, and MLOps – the types of use cases where I connect models, data, and real deployment
Data platforms and enterprise integration
- Oracle DBMS, Teradata, MS SQL Server, Hadoop / Hortonworks – experience with larger-scale data workloads, transfers, and enterprise environments
- Power BI for stakeholder-facing reporting – where analytical output needs to support business decisions, not just technical delivery
AI engineering and automation
- MCP, LLM workflows, PowerShell, Docker, Linux – automation, internal tooling, and implementation of AI use cases in corporate environments
- MS SharePoint and MS Graph API – enterprise integrations and workflow automation in the Microsoft ecosystem
Selected Publications, Projects, and Public Outputs
Academic Publications
- IDEAS / RePEc author profile – publicly traceable working papers and a journal article in econometrics, financial markets, and portfolio analysis
- Structural Distress Index: Structural Break Analysis of the Czech and Polish Stock Markets – journal article focused on structural breaks and market stress
- Multi-Level Analysis of Dynamic Portfolio Formations: Central European Countries and Relationship between Czech and European developed stock markets: DCC MVGARCH analysis – working papers grounded in empirical modeling and data analysis
Public-Sector Strategy Contributions
- Contribution to the strategic document Framework of the Competitiveness Strategy prepared for NERV and the Office of the Government of the Czech Republic
- Collaboration on the chapters Development of Financial Markets and Improving Business Characteristics
Open Source and Writing
- MCP Prompt Broker – routing of instruction profiles for AI agents
- PENB Energy Label Approximation – modeling energy performance of apartments
- WireGuard in Docker – VPN + Nginx reverse proxy architecture
- On the blog, I publish writing about data science, MLOps, and AI in practice; workshops and talks complement this work rather than replacing stronger publication-style outputs
Certifications and Digital Badges
Selected certifications and digital badges from my public Credly profile complement my practical experience in data science, machine learning, and AI. I treat them as secondary validation of expertise that supports project delivery, measurable outcomes, and real implementation work.
Selected Verifiable Credentials
- Applied Data Science with Python – Level 2 (IBM) – Relevant to end-to-end Python work across data analysis, feature engineering, and experimentation.
- Machine Learning with Python – Level 1 (IBM) – Relevant to designing and implementing ML models that need to perform in real business settings.
- Python for Data Science (IBM) – Relevant to Python automation, data workflows, and reproducible analytical practices.
- Spark – Level 1 (IBM) – Relevant to larger-scale data processing and distributed data pipelines.
- Big Data Foundations – Level 2 (IBM) – Relevant to broader data engineering work and modern big data platform foundations.
All listed badges can be publicly verified on Credly.
Education
- Charles University, Prague – Economics (
Mgr., equivalent to a Master’s degree), followed byPhDr.(a Czech post-master’s rigorous degree; best presented internationally as a post-master’s academic qualification, not as a PhD) - Continuous education in ML/AI (Coursera, DataCamp, fast.ai)
Interested in Collaboration?
If you are working on production ML deployment, CRM analytics, or need to identify a realistic AI use case for your company, get in touch. I offer a free 20-minute introductory consultation.