We are looking for an exceptional Quantitative Trader – or an established team – with a demonstrable, attributable P&L track record in equities and equity derivatives in high-frequency or medium-term investment horizons. The ideal candidate should possess a demonstrable history of successful strategy formulation, the acute discernment requisite for identifying and acquiring necessary data, and the engineering proficiency to develop the simulation and research instruments vital for scaling those strategies. This position offers significant impact and substantial autonomy: the successful applicant will assume responsibility for the entire research-to-production lifecycle, collaborating closely with Saccade’s established technology, data, and trading infrastructure teams.

• Own end-to-end systematic trading strategies in equities and equity derivatives – alpha generation, portfolio construction, execution, risk monitoring, and iteration – with direct accountability for live P&L • Identify, procure, and integrate the data the strategy needs • Design and build the simulation/backtesting infrastructure required to evaluate strategies under realistic microstructure, transaction cost, and capacity assumptions • Develop predictive signals and models using necessary techniques the strategy demands – statistical, machine learning, deep learning – across high-frequency and medium-term horizons • Collaborate closely with Software Engineers to productionize research onto Saccade’s low-latency trading platform; • For team applicants: lead and mentor the team within Saccade’s structure, integrate with shared platform components, and contribute to cross-team research culture.

• A demonstrable, attributable record of profitable systematic trading in equities and/or equity derivatives at high-frequency or medium-term horizons – for individuals or for the team as a whole. • Proven ability to source, assess, and seamlessly integrate necessary data for both quantitative research and production trading strategies. • Demonstrated ability to build tools to support the strategy: simulation/backtesting frameworks, research environments and production performance attribution. • Proficiency in programming languages relevant to the strategy is essential, coupled with a strong background in Linux. Candidates must also be comfortable managing and processing extremely large datasets. • Strong statistics, machine learning, and signal construction skills, with a bias toward rigorous out-of-sample validation • Ability to communicate effectively, engaging in productive and constructive debate regarding your own and colleagues' ideas. • Self-motivated, with excellent critical thinking and a risk-based approach to technology, operational, and market risk matters.

CV screening - Interview/test - 2nd round interview