HFT Options Quantitative Researcher

HFT Options and Volatility Trading Research

About DeepFin Research

DeepFin Research is a proprietary high-frequency trading (HFT) firm powered by cutting-edge Deep Learning (DL) and Deep Reinforcement Learning (DRL). We've brought on teammates from Nvidia, DeepMind, CitSec, Graviton, Tower, Jump, and others, and are aggressively working across cutting edge AI research and traditional quant research methods to monetise our AI generated signals across the global financial markets.

The Role

We are looking for exceptional high frequency trading researchers in the options space to monetise our AI driven signals in the global options markets, working in an exceptional team of options researchers.

Responsibilities

Volatility Surface & Pricing Models

• Design, implement, and calibrate ultra-fast vol surface models for equity and index options (e.g., SVI, SABR, Vanna-Volga).
• Integrate models into live trading systems for real-time fitting and quoting.
• Collaborate with quant devs to optimize model performance and stability across exchanges.

Market Making & Execution Research

• Develop and refine high-frequency quoting, hedging, and execution algorithms.
• Optimize order placement, queue position, and fill rates to reduce adverse selection and slippage.
• Strategy Development: Design and backtest systematic intraday strategies specifically targeting equity options, focusing on mean reversion, momentum, and premium decay.
• Analyze market microstructure and order-book dynamics to improve execution logic.

Realized Volatility & Signal Forecasting

• Build and enhance short-horizon realized volatility and spread forecasting models.
• Use high-frequency tick data to identify predictive microstructure and volatility patterns.

Risk & P&L Analytics

• Design real-time delta/gamma/vega hedging frameworks and risk dashboards.
• Dynamic Gamma Hedging: Build automated hedging frameworks to manage the non-linear risks of 0DTE portfolios, optimizing the trade-off between transaction costs and tracking error.
• Conduct PnL decomposition, tracking contributions from alpha, execution, and carry.
• Backtest strategies with realistic latency and cost models.

Ideal Candidate Profile

• Mandatory: Direct experience in high-frequency options trading - preferably market making on equity or index options.
• 5–7 years’ experience in a quant research or trading role at an HFT, prop firm, or leading options market maker.
• Deep understanding of options pricing, Greeks, and market microstructure.
• Experience with vol surface modeling (SVI, SABR, stochastic vol) and real-time model calibration.
• Proven background designing and testing execution logic and hedging systems in production.
• Strong programming ability in C++ and Python; experience with low-latency systems is a plus.
• Advanced degree (Master’s or PhD) in Mathematics, Physics, Statistics, Computer Science, or a related field.