Options Quant Researcher

BHFT

4d•Remote

About The Position

BHFT is seeking an Options Quant Researcher with practical experience in applying volatility models to live trading in TradFi markets. The role involves calibrating volatility surfaces using real market data, handling data imperfections like gaps and latency, and ensuring models meet conditions for smoothness, arbitrage-freeness, and temporal stability. The researcher will tune and debug models under realistic market conditions, design and implement logic for position-driven dynamic surface shaping based on portfolio Greeks, and identify, model, and mitigate residual noise in implied volatility surfaces. This position requires a strong understanding of MFT research and offers the opportunity to work remotely in a dynamic, international technology company with a focus on collaboration and professional growth.

Requirements

Hands-on experience applying volatility models in live trading in TradFi markets.
Practical experience calibrating volatility surfaces on real market data, including handling gaps, latency issues, and using realistic market data.
Medium-Frequency Trading (MFT) research experience is mandatory.
Understanding of how to enforce smoothness, arbitrage-free conditions, and temporal stability.
Ability to tune and debug models under realistic market conditions (bid/ask spreads, noise, and incomplete markets).
Hands-on experience designing and implementing logic for position-driven dynamic surface shaping, including how current portfolio Greeks should influence surface parameters.
Hands-on experience dynamically adapting surface shape based on current exposure.
Ability to identify, model, and mitigate residual noise in implied volatility surfaces, especially near expiry, around illiquid strikes, or in event-driven conditions.
Python (mandatory), with strong use of NumPy, pandas, matplotlib, SciPy, and relevant optimization/ML libraries.
Familiarity with standard quant libraries (QuantLib, or custom volatility tools).

Nice To Haves

High-Frequency Trading (HFT) experience is a plus.
PyTorch / TensorFlow experience is strongly preferred.
Experience with NSE options and/or other TradFi derivatives with margin impact is a major plus.
Familiarity with practical heuristics for surface management.
Working (not just academic) experience applying ML/DL models (e.g., PyTorch, TensorFlow) to this problem.
Understanding of model explainability and risk of overfitting in execution-sensitive environments.
Direct experience in spot/futures vs. options arbitrage.

Responsibilities

Calibrate volatility surfaces on real market data, including handling gaps and latency issues.
Enforce smoothness, arbitrage-free conditions, and temporal stability in volatility models.
Tune and debug models under realistic market conditions (bid/ask spreads, noise, incomplete markets).
Design and implement logic for position-driven dynamic surface shaping, influencing surface parameters based on portfolio Greeks.
Dynamically adapt surface shape based on current exposure.
Identify, model, and mitigate residual noise in implied volatility surfaces, particularly near expiry, around illiquid strikes, or in event-driven conditions.