Independent Data Scientist & AI Engineer (ZZP)
Independent contractor | April 2023 to present
I run an independent data science and AI practice (registered ZZP in the Netherlands), taking on scoped, outcome-based projects for organisations from startups to Fortune 500. I work across production ML, MLOps, agentic AI and RAG, bringing my own methods and tooling and delivering end to end. The specifics of each engagement are kept in confidence.
Selected engagements:
Heineken · independent contract · 2025 to 2026
A full-circle engagement: brought back to a former employer as an independent contractor to extend and improve work I had originally built, and to deliver new production ML alongside senior advisory work. Specifics kept in confidence.
Rabobank · independent contract · 2023 to 2025
Independent contractor delivering production ML and applied GenAI: predictive modelling, experimentation and decision support, taken through to validated, production-grade outcomes. Specifics kept in confidence.
Across engagements · GenAI & agentic systems
- Execution tracing (MLflow) and LLM-as-a-judge evaluation to make agent behaviour observable and testable
- Self-hosting open-weight models (Llama via LM Studio) for privacy-sensitive workflows
- A "small, granular, verifiable" task design philosophy for reliable agents
Lead Data Scientist, Global Commerce & Finance
Heineken · Permanent employee | August 2022 to March 2023
Led an interdisciplinary team of 15+ direct reports (6 data scientists, 2 front end engineers, 2 data engineers, 3 MLOps engineers, and others) and served as interim product owner for the Global Data Harmonisation project.
Delivered Media Mix Modeling, one of the company's most successful analytics projects, with significant captured business value (tens of millions in the first year alone). Highly impactful and used to this day. Also delivered Raw Material Demand Forecasting and Cost Benefit Simulation.
Data Scientist
Heineken · Permanent employee | October 2019 to July 2022
Individual contributor who learned end to end delivery by wearing all the hats, from model architecture and data engineering to project management and stakeholder management. Often served as technical lead. Built solutions that became highly impactful and are used to this day.
Key projects:
Financial KPI Forecasting & Time Series Best Practices: Full end to end ownership covering model architecture, development, MLOps, data engineering, project management, stakeholder management, and delivery scoping. Served as technical lead. Established time series best practices for the organization. Highly impactful, with the team later taking ownership and the system still in use to this day.
Equipment Spare Parts Allocation: Initiated and built with full end to end ownership across technical, project management, and stakeholder management aspects. Served as technical lead. The project became a driving force for operational improvements across the organization with massive opportunity. Highly impactful and still live and used to this day.
Also explored diverse applications including equipment failure detection (IoT), churn prediction, bottle design recommender, warehouse bin allocation, delivery route optimization, and data quality automation. Delivered workshops, trainings, and mentoring across the organization.
Learned by doing everything. Built projects that are highly impactful and used to this day. Later returned as contractor for various improvements on original projects.
Data Scientist
Xccelerated (Xebia) · data-science traineeship, placed at Heineken | September 2018 to October 2019
On Xebia's payroll through the Xccelerated data-science traineeship, placed with Heineken as the client, with the plan to join Heineken full time afterwards (which I did). Joined a newly established analytics department in a fast paced, startup minded environment. Experimented with use cases to figure out what delivered value, facilitated business enablement through hands on analytics trainings, and created materials for analytics translators to support utilization of analytical solutions.
Fin/ID Tech Consultant
UL | July 2017 to September 2018
Contributed to Public Key Infrastructure design, documenting and testing for different governments. Handled pre launch integration testing for leading mobile payment service provider, test automation for bank cards, and technical writing.
Education
Dr. Sc. Physical Chemistry
University of Amsterdam | 2012 to 2017
Research focus: Fundamental understanding of molecular motion under confined conditions. Designed and synthesized molecules, attached them to surfaces, and observed their reaction to contact induced confinement. Used extensive modeling (MATLAB) and a wide variety of experimental methodologies to characterize molecular behavior.
Publications: Multiple papers in highly cited international peer reviewed journals
Skills gained: Scientific rigor, experimental design, extensive modeling, data analysis, technical writing, presenting complex concepts
MSc. Applied Organic Chemistry
University of Zagreb, Croatia | 2010 to 2012 | summa cum laude
BSc. Applied Chemistry
University of Zagreb, Croatia | 2007 to 2010 | cum laude
Technology Stack
Programming Languages
Python • SQL • Bash/Unix scripting
Machine Learning & AI Frameworks
- Classical ML: Scikit-learn, XGBoost, CatBoost, SparkML
- Deep Learning: PyTorch, PyTorch Lightning, fastai
- LLM, RAG & Agents: OpenAI, HuggingFace, LangChain, LangGraph, LlamaIndex, PydanticAI, ChromaDB, FAISS
- LLM Observability & Evaluation: MLflow Tracing, LLM-as-a-judge, custom evals
- Local / Private LLMs: LM Studio, Llama
- Time Series: Prophet, PyMC, DARTS, Nixtla, Stumpy, Ruptures
- Causal Inference: PyMC, CausalNex, DoWhy, NetworkX
- Explainability: SHAP, LIME, ELI5
Data Engineering
Pandas • Polars • PySpark • Databricks
Cloud Infrastructure
- Azure: ADF, Synapse, Databricks, Azure ML
- AWS: S3, Athena, Redshift, ECR, SageMaker
MLOps & DevOps
- Experiment Tracking: MLFlow
- Deployment & Orchestration: Docker, Kubernetes, Databricks Asset Bundles
- CI/CD: GitHub Actions, Azure DevOps
- Quality & Monitoring: Great Expectations, Evidently
- Development: Git, Unit Testing, Integration Testing
Development Tools
FastAPI • Flask • RESTful APIs • Streamlit • Bokeh • Panel
Domain Expertise
Core Machine Learning
Regression • Classification • Clustering • Feature Engineering • Model Selection
Time Series & Forecasting
Univariate & Multivariate Forecasting • Seasonality Modeling • Trend Analysis • Forecast Evaluation
Pattern Recognition & Anomaly Detection
Motif Discovery • Change Point Detection • Outlier Detection • Real-time Anomaly Detection
Causal Inference & Experimentation
Causal Analysis • A/B Testing • Treatment Effect Estimation • Counterfactual Reasoning • Experimental Design
Specialized Applications
- Agentic AI Systems: Autonomous Agents, Agent Debugging, Execution Tracing
- LLM Evaluation & Observability: LLM-as-a-judge, Custom Evals, Retrieval Optimization
- Media Mix Modeling: Marketing Attribution, Channel Optimization, ROI Measurement
- IoT Analytics: Sensor Data Processing, Predictive Maintenance, Equipment Monitoring
- Entity Deduplication: Fuzzy Matching, Record Linkage, Master Data Management
- LLM Applications: RAG Systems, Knowledge Retrieval, Semantic Search