We are a highly agile, AI-first quantitative trading firm built on the belief that speed, domain expertise, and creative research edge can outperform legacy players.
We trade US equity and options markets, focusing on alpha discovery, short-term inefficiencies, and intelligent automation.
Our infrastructure is powered by a custom-built C++ backtesting engine, with AI-driven multi-agent systems that work 24/7 to generate and refine strategies.
With a strong track record of consistent profitability and rapid innovation, we operate at the intersection of quant research, machine learning, and high-performance computing.
We value autonomy, intellectual curiosity, and measurable impact. If you're obsessed with options and love finding edge in noisy markets, we want to hear from you.
We’re looking for a highly analytical and driven Options Researcher to join our fast-growing quant trading firm. Your primary responsibility will be to research, prototype, and validate systematic options trading strategies — across intraday, swing, and 0DTE horizons. You’ll work closely with our core team to convert data into edge, and edge into alpha.
This is a hands-on, research-heavy role suited for someone passionate about market microstructure, volatility modeling, and options-based strategy development. You’ll be empowered to test hypotheses quickly, use real-world trade logs to refine strategies, and build robust statistical frameworks to evaluate performance.
- Responsibilities
Design, research, and backtest quantitative options strategies (including volatility arbitrage, relative value, spread-based, and directional setups).
Work with raw historical and real-time data (e.g., option chains, implied volatility, Greeks, open interest).
Identify edge through data exploration, hypothesis testing, and statistical modeling.
Build and validate predictive features using options and underlying market data.
Collaborate with execution and data engineering teams to translate research into production-ready models.
Monitor live strategies, analyze performance, and iterate for robustness.
Develop tooling for strategy simulation, risk evaluation, and capital optimization.
- Requirements
Solid background in statistics, financial engineering, mathematics, or computer science.
Experience with systematic options trading — directional or non-directional (vol/IV, spreads, 0DTE, etc.).
Strong coding skills (Python required; C++/Rust a plus).
Familiarity with options data structure (IV surface, Greeks, OI, etc.) and volatility modeling (e.g., GARCH, SABR, local vol).
Prior experience using backtesting frameworks or developing custom ones.
Comfort working in a fast-paced, research-oriented environment with a focus on execution.
Bonus: exposure to machine learning methods for options signal generation.
Kindly note that only shortlisted candidates will be notified.
