Summary
Wouter Van Amstel is a data scientist and derivatives trader with 11+ years blending quantitative finance and machine learning across trading desks and product teams. He has deep, hands-on experience in volatility arbitrage, dispersion and relative value trading alongside building scalable NLP and ML systems using Spark/MapReduce and databases like PostgreSQL, MongoDB, ElasticSearch and Cassandra. Wouter has led data science teams to productionize language-driven models in radiology and developed novel NLP methodologies that retained major enterprise clients. Technically fluent in Python, C/C++ and R, he pairs model research with production engineering—implementing test-driven code and collaborating closely with product and UX. Based in San Francisco, he brings engineering discipline from geotechnical and mining degrees to both market microstructure problems and large-scale unstructured data. An uncommon strength is his ability to translate cross-disciplinary insights (trading intuition, NLP, and systems engineering) into robust, revenue-driving solutions.
12 years of coding experience
8 years of employment as a software developer
MS, Geotechnical Engineering, MS, Geotechnical Engineering at University of California, Berkeley
MS, Mining & Petroleum Engineering, MS, Mining & Petroleum Engineering at Delft University of Technology
Dutch, French, German