About me

Hi there! I am Jeroen van Hoof, a software engineer living in Amsterdam.

I have a background in Computer Science and experience with DevOps, data engineering, machine learning, data science and web development.

Contact details



TU/e - Master Computer Science 2015 – 2018 Eindhoven, Netherlands
A large part of this program was focused on machine learning, deep learning, data mining, and data engineering.
Relevant courses:
  • Data Engineering
  • Deep Learning
  • Machine Learning
  • Data Mining
  • Visualization
  • Geometric Algorithms
  • Database Technology
TU/e - Bachelor Web Science 2011 – 2015 Eindhoven, Netherlands
Web Science is a track within Computer Science and Engineering. It is concerned with the user side of software and the web. This part is supported by psychology, economics and sociology courses, on top of the standard computer sciences courses. The major also involves a lot of data science and analytics, mostly on using data from the web and social media.
Relevant courses:
  • Web Technology
  • Visualization
  • Web Analytics
  • Software Engineering
  • Algorithms
  • Artificial Intellligence
  • Interactive Intelligent Systems

Work Experience

Rabobank - Tech Lead Full-time Dec 2021 – Present Utrecht, Netherlands
  • Set up required infrastructure, pipelines and application for using Quantexa's entity resolution capability.
  • Provided input on proof-of-value by testing future fit and performance, determining use cases, and working out scenarios and required resourcing.
Technology stack:
  • Scala
  • Kubernetes
  • Bicep
  • Kibana
  • Spark
  • Azure DevOps
  • Databricks
ABN AMRO - Data Engineer Full-time Apr 2020 – Nov 2021 1 yr 8 mos Amsterdam, Netherlands
  • Developed a data platform (based on Quantexa) that creates interactive and expandable networks of customers, companies, products and transactions to provide better context for financial fraud investigations.
  • Used entity resolution to extract entities from data and graph analytics methods to build a model that helps us to create a more complete and detailed overview of each client, and provides better insights to do risk scoring.
  • Responsibilities involved a mix of data engineering, development, operations and data science, as well as coordinating the implementation of controls related to continuous integration, delivery, testing, monitoring and operations and security, to ensure that the application and the team is in control and complies with all the standards of the bank.
Technology stack:
  • Scala
  • Kubernetes
  • Spark
  • Azure Data Factory
  • Azure DevOps
  • Databricks

Gold Tech Champions Award:
ABN AMRO - Machine Learning Engineer Full-time Sep 2019 – Apr 2020 8 mos Amsterdam, Netherlands
  • Optimized data science projects for transaction monitoring and customer risk assessment for production.
  • Adoption and migration (from mostly manually-executed SAS scripts) to new advanced analytics platform based on Azure DevOps, Databricks, Data Factory, etc.
Technology stack:
  • PySpark
  • Databricks
  • Python
  • SQL
Wolfpack - Data Scientist Part-time Aug 2017 – Oct 2017 3 mos Eindhoven, Netherlands
For a medical app, I worked on a functionality that allows users to add their medicines to the app by scanning and parsing information directly from a medicine box via their phone's camera, using optical character recognition (OCR), natural language processing (NLP) with entity recognition and grammar parsing, and simple object detection using OpenCV.
Technology stack:
  • NLTK
  • OpenCV
  • MongoDB
  • Python
  • Google Cloud Vision

MedApp development of AI

The-Line Smart Solutions - Web developer Part-time Jul 2015 – Aug 2016 1 yr 2 mos 's-Hertogenbosch, Netherlands
I developed multiple web applications and mobile applications for different clients, and I worked with a team to build a complete infrastructure for a vehicle sharing startup, their tenants and their tenants' users. This included web-based management tools to manage tenants and their users, vehicles, promotions, etc. and a mobile app that allows users to unlock and rent a vehicle.
Technology stack:
  • Angular
  • Laravel
  • Docker
  • Sass
  • PHP
  • JS



Quantexa technical academy Jan 2022 Quantexa
Awarded to Data Engineers who have completed the Quantexa Technical Academy.
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AI & Data science 3-star program Aug 2021 GAIN - Global AI Network
The 3-star AI & Data Science program is aimed at data and analytics professionals.
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ITIL v4 Foundations Jul 2021 Microsoft Credential ID: GR671286166JV
IT service management through an end-to-end operating model for the creation, delivery and continual improvement of tech-enabled products and services.
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Azure Data Engineering Associate Jun 2021 Microsoft
Skills and expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions.
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Big data analysis with Scala and Spark May 2020 Coursera
Manipulating data and data analysis with Scala and Spark.
See credential


LanguagesPython, Javascript, Scala, Java, PHP
Machine learningLightGBM, Scikit-learn, Keras (Tensorflow, Pytorch), NLTK, Word2Vec, OpenCV, Spark MLlib
FrontendVue, React, Angular
BackendFlask, Django, Laravel
DevOpsCI/CD, Kubernetes, Linux, Git, Docker, Bicep, Jenkins
Data EngineeringSpark, ETL, SQL, Data Factory, Databricks, Airflow, MongoDB


Open Source Project: Foronoi Code Docs Pypi
Foronoi is a Python package that I developed that implements a sweep line algorithm for Voronoi diagrams. It scans top down over a set of points and traces out the borders along the way while scanning for the next point. My package includes a visualization API, the ability to clip the diagram inside polygon bounding-boxes and some other extras.
Master Thesis: Automated Machine Learning Thesis Paper Code

My master thesis was on the topic of Automated Machine Learning, which is the field that strives to automate the data scientist's process of finding and optimizing the right machine learning model. We do so by testing machine learning models with different configurations and observing their performance to determine interesting configurations to test next. This can be done in many different ways: genetic programming, bayesian optimization, bracket-wise competition, etc.

I developed a new method closely related to bayesian optimization that uses much less time to decide interesting configurations, and converges faster to the optimum.

Bachelor End Project: TravelMatch
Developed an Android/iOS app that learns what your personal "travel DNA" is, based on the liking and disliking of travel images, and then recommends holiday destinations using affiliate links.
  • Ionic
  • Angular
  • Django
  • Python