Requirements
Requires a strong quantitative background in maths, physics, financial engineering, or computer science with a deep understanding of options pricing theory and practical limitations of models like Black-Scholes and stochastic vol models. Must have hands-on experience building execution models or systematic options strategies, familiarity with crypto derivatives markets, strong Python skills, and a rigorous approach to backtesting options strategies. Senior candidates should have a live, attributable track record in options market making, vol arb, or systematic derivatives trading.
Description
AlgoQuant Asset ManagementDubai (preferred) London New York – Reports to Head of Research – Rolling startAbout AlgoQuantAlgoQuant Asset Management is a multi-strategy digital asset manager allocating capital across25+ internal and external quantitative trading pods. Founded in 2018, we have evolved into aninstitutional platform combining trading edge with strong governance and advanced technology,serving family offices and institutional investors globally.The roleWe are hiring an Options Execution Researcher to build and optimise systematic execution andpricing models for digital asset derivatives. This is a role at the intersection of quantitative researchand live trading — you will develop the models that determine how we trade options, not justanalyse them. You will own the full stack from theoretical pricing to live execution logic, workingclosely with portfolio managers and engineers to move from research into production.This role is for someone with genuine options intuition: you think in vol surfaces, understand theGreeks under pressure, and have a track record of turning derivatives theory into executable,capital-efficient strategy.ResponsibilitiesBuild and maintain options pricing and valuation models calibrated to digital asset volmarketsDevelop execution algorithms for options and structured derivatives: entry/exit timing,hedging logic, and delta managementResearch volatility dynamics across crypto markets — term structure, skew, realised vsimplied, and cross-asset relationshipsAnalyse microstructure on options venues to improve fill quality and reduce execution costsConstruct and maintain backtests for options strategies with accurate handling of pathdependency, margin, and transaction costsCollaborate with engineers to deploy execution models into live infrastructureMonitor live strategy Greeks and P&L attribution in real time, iterate on models as marketsevolveWhat we are looking forStrong quantitative background in maths, physics, financial engineering, or computerscienceDeep understanding of options pricing theory — Black-Scholes, stochastic vol models(Heston, SABR, local vol), and their practical limitationsHands-on experience building execution models or systematic options strategies, either ata trading firm, hedge fund, or structured products deskFamiliarity with crypto derivatives markets (Deribit, OKX, Bybit) and their structuraldifferences from TradFi options marketsStrong Python; C++ a significant plus for latency-sensitive execution workRigorous approach to backtesting options strategies — experienced with the pitfalls of pathdependency, vol model overfitting, and slippage estimationSelf-directed with a strong sense of ownership — comfortable driving research from idea toproduction without hand-holdingFor senior candidates: a live, attributable track record in options market making, vol arb, orsystematic derivatives trading