Education
MSc in Computer Science: Data Science (incl. Pre-Master)
2024 - Aug 2026 (expected)
Leiden University, The Netherlands
MSc thesis: graph-based anomaly detection in ERP accounting data using an unsupervised Graph Autoencoder with a GraphSAGE encoder, plus explainability methods for transparent, auditable anomaly interpretation.
Selected Coursework
- Deep Learning
- Cloud Computing
- Reinforcement Learning
- AutoML
- Social Network Analysis
- System & Software Security
- Software Development & Product Management
2024 - 2026
BSc in Electrical Engineering, Minor in Embedded Systems
2016 - 2020
Hogeschool Rotterdam, The Netherlands
2016 - 2020
Professional Experience
MACH Technology, The Netherlands
2023 - Present
Full-Stack Data Analyst (part-time, alongside MSc)
Designing, building and maintaining internal data and web applications (React/Flask), including a tool used by ~8 stakeholders to visualize demand and idle stock across two production sites. Applying machine learning to classify component demand patterns and modelling component relationships as a network (Python, NetworkX) on top of SQL Server data. Collaborating across procurement, sales support, work planners, IT and R&D.
2023 - Present
MACH Technology, The Netherlands
2020 - 2023
Hardware & Embedded Software Engineer R&D
Delivered around ten prototypes and final products, such as home trainers, check-in systems and connected devices, covering the full cycle from firmware to PCB design and manufacturing. Worked directly with clients on requirements engineering, acting as product owner on several projects.
2020 - 2023
MSc Thesis
“The Irregular Neighbourhood”: Explainable Graph-Based Anomaly Detection in ERP Accounting Data
Graph Neural Networks
Leiden University, inspired by an industry research proposal from KPMG NL
Graph Neural Networks
For forensic audit, detecting an anomaly is not enough: every flagged transaction needs transparent, defensible evidence. In my thesis I designed a reproducible pipeline that transforms raw journal entries into a heterogeneous graph connecting entries, general-ledger accounts and profit centers, and scores each entry with an unsupervised Graph Autoencoder (GraphSAGE encoder). A built-in explainability layer answers the two questions an auditor actually asks: which relationship made this entry suspicious, and which field. The pipeline outperforms both tabular and graph baselines and detects relational anomalies that are invisible to tabular models.
Highlights
- Heterogeneous graph of 533K nodes and 2.1M edges, built from raw ERP journal entries
- Unsupervised GraphSAGE autoencoder with a leak-free three-phase evaluation protocol
- Detects local anomalies that tabular models cannot see, using relational context alone
- Integrated explainability: relational (occlusion) and feature-level evidence per flagged entry
- Validated on a synthetic SAP FI/CO benchmark and real-world vendor payment data
Case Study
Data-Driven Inventory Optimization
Full-Stack Data Analysis
Strategic Sourcing Team
Data Analysis
In my current project, I built a full-stack web application that interfaces with our company's MRP systems to drive smarter inventory decisions. By extracting data with SQL, mapping complex relationships between components using Python’s NetworkX library, and recommending viable alternatives, my work helps reduce slow-moving and obsolete parts. This initiative not only optimizes inventory but also supports a lean, just-in-time strategy across production sites.
Highlights
- SQL-driven data extraction from MRP systems
- Network modeling with Python’s NetworkX
- Development of a full-stack web application
- Strategic sourcing to reduce inventory of obsolete parts
Skills & Toolkit
Programming
Python, SQL, C/C++, TypeScript, React, Flask, Git, MATLAB, LaTeX, Linux/SSH
Data Science
Machine learning, deep learning, graph neural networks (GraphSAGE, GAT, autoencoders), anomaly detection, explainable AI, statistical modelling, forecasting
Cloud & Methods
Microsoft Azure (Functions, App Service, Blob Storage), containers, LLM/API integration, Agile/Scrum, JIRA, MRP systems
Engineering
Embedded systems design, PCB design, wireless communication, low-level protocols, requirements engineering, firmware
Languages
Dutch (native), English (C1 advanced), German (beginner, actively learning)
Interests
Outside of work, you’ll often find me challenging myself on the climbing wall or out on a long mountain hike.
Whether it’s an indoor climb or a rugged mountain trail, I see each ascent as a puzzle that challenges both my body and my mind.
I also love camping, spending nights under the stars and enjoying the simplicity.
These activities keep me grounded, boost my creativity, and remind me why I love tackling challenges every day.
Background
Data and Tech
Technology has been a part of my life since I was little, thanks to my dad and his world of electronics.
My fascination with physics naturally led me to study Electrical Engineering, and my early career in embedded systems taught me the ropes.
Somewhere along the way, I discovered the true power of data, which really changed the game for me.
Combined with my growing interest in business values and a hunger for bigger challenges, I kicked off my Master’s in Data Science.
And here I am today, living in the fast lane of data and tech, with a lot of passion and motivation.
I embrace all the challenges and work on projects I never thought possible, like training a graph
neural network to find anomalies in half a million accounting records for my thesis, while running
my own cloud data pipeline on the side.
I'm enjoying every twist and turn alongside an awesome community of friends who share the same passion.
Other Projects
Switchback: Cloud-Deployed Automated Data Pipeline
Cloud & LLM Engineering
End-to-end pipeline on Microsoft Azure that ingests data from multiple public APIs, deduplicates by content hash, and scores relevance with an LLM (Anthropic Claude) using prompt caching and strict-JSON output to bound cost. Runs live with a Flask dashboard, scheduled daily runs, email alerts and hardened authentication.
Cloud & LLM Engineering
Data Science in Social Networks
A comparison study on Node Embeddings for the Social Network Science course at Leiden University.
Data Science
Requirements Engineering
Enhanced requirements engineering standards at MACH Technology using the Volere Method.
Requirements Engineering
Embedded Systems
Developed prototypes for a 400VAC switch controlled wirelessly via the OOK (On/Off Keying) protocol.
Embedded Systems
Embedded Systems
Developed C code for a high-power BLDC motor and created a matching Python-based GUI.
Embedded Systems
Hardware Engineering
Designed printed circuit boards (PCB) for manufacturing using Altium software (2D and 3D tools).
Hardware Engineering
Contact Me
Feel free to reach out for collaborations. Open for full-time data science roles from October 2026.