Jonathan Lloyd is a Quantitative Solutions Engineer with a PhD in Economics and a CQF, combining ten years of experience marrying academic rigour with production-grade Python and PostgreSQL engineering for quantitative finance. He has designed analytical libraries, backtested models and integrated datasets from Refinitiv, Bloomberg and Eikon to support portfolio managers on market risk, ESG reporting and investment decisions. His background spans hands-on treasury and ALM work at Triodos and a published fiscal-budgeting paper with the UK Treasury based on a public spending microsimulation model he co-developed, highlighting his ability to turn research into policy-relevant tools. Equally comfortable in research, teaching and delivery, he pairs machine learning and applied econometrics expertise with practical software engineering experience (including work as a software engineer at Depop) to move models from ideation to production.
11 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Economics, Pass with minor corrections, Doctor of Philosophy - PhD, Economics, Pass with minor corrections at Cardiff University / Prifysgol Caerdydd
Graduate Summer School, High-Dimensional Time Series Models: Big Data and Machine Learning, Graduate Summer School, High-Dimensional Time Series Models: Big Data and Machine Learning at Barcelona Graduate School of Economics
Contributions:4 reviews, 158 commits, 66 PRs in 2 years
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