Options Quant Researcher - Volatility Models
Anywhere in the worldFull-Time
Salary not disclosed
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Job Details
- Required Skills
- PythonNumpyPyTorchPandasTensorflow
Requirements
- Hands-on experience applying volatility models in live trading in TradFi markets
- Practical experience calibrating volatility surfaces on real market data
- Experience handling gaps, latency issues for realistic market data
- MFT'ish research is a must; HFT is nice to have
- Understand how to enforce smoothness, arbitrage-free conditions, and temporal stability
- Able to tune and debug models under realistic market conditions (bid/ask spreads, noise, incomplete markets)
- Python (mandatory)
- Strong use of NumPy, pandas, matplotlib, SciPy, and relevant optimization/ML libraries
- Familiarity with standard quant libraries (QuantLib, or custom volatility tools)
- PyTorch / TensorFlow experience (strongly preferred)
- Experience with NSE options and/or other TradFi derivatives with margin impact (major plus)
- Familiarity with practical heuristics for surface management (nice to have)
- Working (not just academic) experience applying ML/DL models (e.g., PyTorch, TensorFlow) to this problem (nice to have)
- Understanding of model explainability and risk of overfitting in execution-sensitive environments (nice to have)
- Direct experience in spot/futures vs. options arbitrage (nice to have)
Responsibilities
- Design and implement logic for position-driven dynamic surface shaping
- Determine how current portfolio Greeks (vega, gamma, skew) should influence surface parameters such as skew, curvature, and wing behavior
- Dynamically adapt surface shape based on current exposure
- Identify, model, and mitigate residual noise in implied volatility surfaces, especially near expiry, around illiquid strikes, or in event-driven conditions
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