Options Execution Researcher

About AlgoQuant Asset Management

AlgoQuant Asset Management, established in 2018, is a multi-strategy digital asset management firm that allocates capital across more than 25 internal and external quantitative trading teams. Our institutional platform blends trading expertise with robust governance and cutting-edge technology, catering to family offices and institutional investors worldwide. We are headquartered preferably in Dubai, with additional operations in London and New York.

Role Overview

We are seeking an Options Execution Researcher responsible for designing and refining systematic execution and pricing models for digital asset derivatives. This position bridges quantitative research with live trading, involving development of models for how options are traded rather than solely analyzed. The researcher will manage the entire spectrum, from theoretical pricing models to live execution strategies, collaborating closely with portfolio managers and engineers to deploy these models into production.

Key Responsibilities

• Develop and sustain options pricing and valuation models tailored to digital asset volatility markets.
• Create execution algorithms for options and structured derivatives focusing on entry and exit timing, hedging strategies, and delta management.
• Investigate volatility behaviors across cryptocurrency markets, including term structures, skew, realized versus implied volatility, and inter-asset connections.
• Analyze microstructure of options trading venues to enhance fill rates and decrease execution costs.
• Construct and maintain backtesting frameworks for options strategies, accurately considering path dependency, margin requirements, and transaction costs.
• Work in tandem with engineering teams to transition execution models into live trading systems.
• Continuously monitor live strategy Greeks and profit & loss attribution, refining models responsively as market conditions evolve.

Candidate Profile

• Solid quantitative foundation in mathematics, physics, financial engineering, or computer science.
• Comprehensive knowledge of options pricing theories including Black-Scholes and stochastic volatility models such as Heston, SABR, and local volatility models, alongside an awareness of their practical constraints.
• Practical experience developing execution models or designing systematic options strategies within trading firms, hedge funds, or structured products divisions.
• Familiarity with cryptocurrency derivatives platforms like Deribit, OKX, and Bybit, understanding their structural differences versus traditional finance options markets.
• Proficiency in Python programming; knowledge of C++ is highly desirable for tasks demanding low-latency execution.
• Disciplined and sophisticated approach to backtesting, recognizing challenges like path dependency, volatility model overfitting, and slippage estimation.
• Self-motivated with a strong ownership mentality, capable of independently leading research from concept through deployment.
• For more senior applicants: demonstrated, verifiable track record in options market making, volatility arbitrage, or systematic derivatives trading.