AlgoQuant Asset Management
Dubai (preferred) · London · New York – Reports to Head of Research – Rolling start
About AlgoQuantAlgoQuant Asset Management is a multi-strategy digital asset manager allocating capital across
25+ internal and external quantitative trading pods. Founded in 2018, we have evolved into an
institutional platform combining trading edge with strong governance and advanced technology,
serving family offices and institutional investors globally.
The role
We are hiring an Options Execution Researcher to build and optimise systematic execution and
pricing models for digital asset derivatives. This is a role at the intersection of quantitative research
and live trading — you will develop the models that determine how we trade options, not just
analyse them. You will own the full stack from theoretical pricing to live execution logic, working
closely 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 the
Greeks under pressure, and have a track record of turning derivatives theory into executable,
capital-efficient strategy.
Responsibilities
● Build and maintain options pricing and valuation models calibrated to digital asset vol
markets
● Develop execution algorithms for options and structured derivatives: entry/exit timing,
hedging logic, and delta management
● Research volatility dynamics across crypto markets — term structure, skew, realised vs
implied, and cross-asset relationships
● Analyse microstructure on options venues to improve fill quality and reduce execution costs
● Construct and maintain backtests for options strategies with accurate handling of path
dependency, margin, and transaction costs
● Collaborate with engineers to deploy execution models into live infrastructure
● Monitor live strategy Greeks and P&L attribution in real time, iterate on models as markets
evolve
What we are looking for
● Strong quantitative background in maths, physics, financial engineering, or computer
science
● Deep understanding of options pricing theory — Black-Scholes, stochastic vol models
(Heston, SABR, local vol), and their practical limitations
● Hands-on experience building execution models or systematic options strategies, either at
a trading firm, hedge fund, or structured products desk
● Familiarity with crypto derivatives markets (Deribit, OKX, Bybit) and their structural
differences from TradFi options markets
● Strong Python; C++ a significant plus for latency-sensitive execution work
● Rigorous approach to backtesting options strategies — experienced with the pitfalls of path
dependency, vol model overfitting, and slippage estimation
● Self-directed with a strong sense of ownership — comfortable driving research from idea to
production without hand-holding
● For senior candidates: a live, attributable track record in options market making, vol arb, or
systematic derivatives trading
