Jonathan Lloyd is a quantitative analyst and AI/ML practitioner with a PhD in Economics, a CQF, and over a decade of professional experience bridging research-grade econometrics and production-grade engineering. He designs, builds and maintains analytical Python libraries and PostgreSQL systems, develops and backtests quantitative strategies, and integrates data from Refinitiv, Bloomberg and Eikon into robust workflows. Comfortable on both buy- and sell-side desks, he partners with portfolio managers on market-risk metrics, ESG reporting and investment decisioning while translating technical outputs for stakeholders. His academic background includes published fiscal microsimulation work for the Treasury and extensive teaching experience, evidencing strong communication and reproducible research practices. Trained in high-dimensional time series and deep learning, he brings a rare combination of macroeconomic theory, applied machine learning and practical finance operations. Based in London, he’s currently seeking new quantitative finance and research challenges where rigorous modelling meets production deployment.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Economics, Doctor of Philosophy - PhD, Economics at Cardiff University / Prifysgol Caerdydd
CQF, Finance, CQF, Finance at Fitch Learning
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 School of Economics
Contributions:4 reviews, 158 commits, 66 PRs in 2 years
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