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Research Engineer at Google DeepMind
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Always looking for ways to keep learning, curiosity has driven me to study the inner workings of our universe using computers and abstract mathematics. After my PhD in theoretical physics (string theory) I started working in industry doing applied machine learning for fraud prevention, marketing and cyber security. More recently I've been working on fundamental challenges in artificial intelligence.
University of Amsterdam
Doctor of Philosophy (Ph.D.), Theoretical Physics - String Theory
January 1, 2010 – January 1, 2014
University of Amsterdam
Master of Science (MSc), Theoretical Physics
January 1, 2009 – January 1, 2010
McGill University
MSc Research Exchange, Theoretical Physics
January 1, 2008 – January 1, 2009
University of Amsterdam
Bachelor of Science (BSc), Physics and Astronomy
January 1, 2003 – January 1, 2007
Google DeepMind
Research Engineer
July 1, 2021 – Present
London, England, United Kingdom
Microsoft
Senior Applied Researcher
December 1, 2020 – May 1, 2021
Microsoft
Applied Researcher
March 1, 2020 – December 1, 2020
Microsoft
Data Scientist - Machine Learning
August 1, 2018 – March 1, 2020
Booking.com
Senior Data Scientist - Machine Learning • Paid Search
July 1, 2017 – August 1, 2018
Booking.com
Data Scientist - Machine Learning • Fraud Prevention
February 1, 2016 – July 1, 2017
MobPro, Mobile Professionals
Data Scientist - Machine Learning
January 1, 2015 – February 1, 2016
Amsterdam Area, Netherlands
Import.io
Data Science Fellow - S2DS Program
August 1, 2014 – September 1, 2014
London, United Kingdom
University of Amsterdam
Doctoral Researcher - String Theory
September 1, 2010 – August 1, 2014
Amsterdam Area, Netherlands
Introduction to Data Science
University of Washington
June 24, 2026 – Present
Scalable Machine Learning with Apache Spark
edX
June 24, 2026 – Present
Introduction to Functional Programming
Delft University of Technology
June 24, 2026 – Present
Algorithms: Design and Analysis, Part 1
Stanford University
June 24, 2026 – Present
S2DS - Science to Data Science
Pivigo Academy London
June 24, 2026 – Present
Accelerated Deep Learning with Tensorflow
NVIDIA
June 24, 2026 – Present
Machine Learning
Stanford University
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a research-oriented or advanced data science role, particularly within organizations that value innovation, open-source contributions, and continuous learning. Their experience across various domains (cyber security, fraud prevention, ad tech) and their involvement in teaching/mentoring align well with a collaborative and knowledge-sharing environment. The transition from theoretical physics to applied data science and AI showcases a strong drive for practical impact and continuous skill development.
Soft Skills & Operational Fit
The candidate's experience in leading efforts to open-source frameworks and giving internal workshops suggests strong communication, mentorship, and collaboration skills. Their academic background in theoretical physics implies a high capacity for complex problem-solving and independent research. The diverse project portfolio indicates adaptability and a proactive approach to learning and applying new technologies.