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Options Quant Researcher

External
Bhft logoBhft · Dubai, UAE
Full-timeRemote1mo ago30+ days old, may be filled
MatplotlibNumPyPandasPythonPyTorchTensorFlow
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Requirements

  • 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

Benefits

Experience a modern international technology company without the burden of bureaucracy.Collaborate with industry-leading professionals, including former employees of Tower, DRW, Broadridge, Credit Suisse, and more.Enjoy excellent opportunities for professional growth and self-realization.Work remotely from anywhere in the world with a flexible schedule.Receive compensation for health insurance, sports activities, and non-professional training.Health insuranceRemote work optionsFlexible schedule

Additional Information

We're looking for a Quant Researcher with hands-on experience applying volatility models in live trading in TradFi markets. We expect the candidate to: Have practical experience calibrating volatility surfaces on real market data Including handling gaps, latency issues and so on to effectively use realistic data available in the market MFT'ish research is must. HFT is nice to have. Understand how to enforce smoothness, arbitrage-free conditions, and temporal stability Be able to tune and debug models under realistic market conditions - including bid/ask spreads, noise, and incomplete markets Design and implement logic for position-driven dynamic surface shaping, including: How current portfolio Greeks (vega, gamma, skew) should influence surface parameters such as skew, curvature, and wing behavior Hands-on experience is required for 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) PyTorch / TensorFlow experience (strongly preferred) Experience with NSE options and/or other TradFi derivatives with margin impact is a major plus.

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