Dr. David Fussell

Head of Data Science
Data Science
/static/boronia-6f3fd0ccdc4d19e4faf7ecdd97c5906c.jpeg
data:image/jpeg;base64,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
David is an experienced data scientist who has taken a range of systems to production, is driven to stay at the forefront of research, and keen to bring machine learning to new applications.

Prior to joining Daisee, David was a Research Scientist at ClearAlts where he: developed a transparent and robust global macro index exploiting factor-based asset-timing; discovered state-reversion, the predictable alternation between trend and mean-reversion; developed delta carry, the short-term prediction of returns by fluctuations in the futures slope.

David was also a Research Scientist at Boronia Capital where he: developed a high-frequency multi-variate orderbook system with an integrated market-impact model (regression); invented a range-based variance-ratio filter that returned to profitability the core volatility strategy (bootstrapping); created a short-term mean-reversion system from the ground-up to complement the core strategy; developed a sector-based trend-following system to complement the existing trend-following systems; and also worked on execution algorithms (Q-learning), pairs-trading/stat-arb strategies (principal component analysis) and risk premia strategies (factor analysis).

David has a Doctor of Philosophy from the University of Sydney in the field of Theoretical Physics.