Matthew Smith

NLP & LLM Researcher In Quantiative Finance

Barcelona, Catalonia, Spain
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Summary

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Senior
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Top School
Matthew Smith is an AI researcher and quantitative finance specialist with a PhD in Machine Learning applications to economics and finance and nine years of experience bridging academia, industry, and high-performance computing. He develops and deploys ML models and apps for banking and financial advisory—recently building agentic LLM-based market models and production FastAPI/LangChain services at Esade. His background includes postdoctoral research and teaching in asset pricing, pandemic-related GIS/ML products at the Barcelona Supercomputing Center (two Nature publications), and hands-on ML consulting at Deloitte. Comfortable in R, Python, C++ and Shiny, he turns research-grade methods into client-ready tools and classroom-ready curricula. Based in Barcelona, he maintains an active GitHub and blog where he documents reproducible finance/ML work, reflecting a habit of shipping research as usable software. Notably, his work spans from classical asset-pricing models to cutting-edge agentic LLM experimentation, combining deep domain knowledge with production engineering.
code9 years of coding experience
job5 years of employment as a software developer
bookBachelor's Degree, Business Management, Bachelor's Degree, Business Management at Swansea University / Prifysgol Abertawe
bookDoctor of Philosophy - PhD, Economics, Doctor of Philosophy - PhD, Economics at Universidad Complutense de Madrid
bookMaster's degree, Economics, Master's degree, Economics at Universitat de Barcelona
languagesEnglish, Spanish
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Github Skills (51)

data-import9
resampling8
r8
sec7
charting7
finance7
climate-science7
quantitative7
algorithmic-trading6
shiny6
time-series6
r-programming5
keras5
python5
financial-data5

Programming languages (4)

JavaRC++HTML

Github contributions (5)

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Contributions:24 pushes, 1 branch in 7 years 9 months
msmith01/Thesis-Chapter-One

Sep 2018 - Jun 2021

Contributions:8 pushes, 1 branch in 2 years 9 months
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