Option Quant

Location: Remote (with presence in London & Milan)

About the Role
Were seeking a driven technically strong Options Quant to join our growing trading team.
Youll design and enhance pricing execution and risk models for options across multiple markets with direct impact on live performance.
This role blends quant research coding and market intuition. Youll collaborate with traders and engineers to develop production-ready models and strategies within a high-talent low-ego environment.

Tasks

Key Responsibilities
Research design and implement options pricing and volatility models.
Build and optimize simulation backtesting and research pipelines.
Analyze market microstructure and volatility dynamics to identify trading opportunities.
Design and maintain core quant components - pricing engines execution algorithms and risk controls.
Run post-trade analysis to refine models and improve performance.
Apply advances in ML optimization and statistics to live trading problems.

Requirements

Requirement:
37 years in quant research derivatives pricing or options trading (crypto preferred).
Strong grasp of mathematics statistics and optimization.
Deep understanding of options theory risk management and execution logic.
Self-directed analytical and collaborative - thrives in fast-paced flat environments.
Programming skills in high level languages (Python).

Not a requirement but a plus:
Programming skills in low level languages (GoLang).
Prior experience in algorithmic or high-frequency trading.
Experience with signal extraction from high-frequency or noisy data.
Understanding of crypto markets and decentralized finance.

Benefits

Why Join Us
Immediate impact: Your models go live fast with direct P&L visibility.
Elite peers: Work alongside experienced traders researchers and engineers.
Top-tier rewards: Competitive compensation with strong performance upside.
Community: Regular on-sites in London/Milan and annual offsite in a luxury location.

Please note that we will test your quantitative and programming skills in-person.