Options Quant Researcher

Were looking for a Mid-Senior Quant Researcher specializing in options with hands-on experience turning original strategy ideas into fully automated production strategies in TradFi markets working close to trading throughout.

The mandate is to help build a single unified options quoting/pricing engine that prices across all strikes expiries and underlyings driven by a relative-value view on implied volatility across instruments in a unified delta order book (spot / futures / option legs).
The alpha stack spans:

• Index vol arbitrage (IV differences between instruments) single-stock IV ranking
• Calendar / term-structure spreads
• Skew (smile) arbitrage
• Implied Volatility vs. Realized Volatility
• Correlation via dispersion trading

We expect the candidate to do some subset of these things:

• Own end-to-end options strategy research: hypothesis data modeling backtesting production live monitoring and iteration
• Work on Relative Value Statistical Arbitrage and Spread Trading strategies specific to the options universe (the stack above)
• Build and own the volatility fitter the signals sit on  calibrating arbitrage-free temporally stable surfaces (SVI/SSVI or a proposed alternative) on realistic data (wide bid/ask missing strikes gaps latency) with attention to residual noise near expiry / illiquid strikes / events
• Translate strategy output into execution  routing a target delta-order across option legs to minimize Greek risk with inventory-aware quoting that shifts price/size against live vega/gamma/skew and awareness of options microstructure (spreads queue adverse selection latency)
• Build and maintain mid-frequency (MFT) fully automated strategies with a strong live-performance focus
  • Track record of deploying fully automated strategies with Sharpe > 2 (or demonstrable equivalent risk-adjusted performance)
• Design robust signal research pipelines (feature engineering labeling validation regime analysis)
• Develop realistic backtests and live-simulation frameworks accounting for slippage spreads latency partial fills and market impact
• Work in tight feedback loops with trading and execution to improve PnL robustness and risk-adjusted performance
• Debug and tune research outputs under live conditions: data issues execution artifacts microstructure noise and changing market regimes

Qualifications :

• Python (mandatory) strong use of NumPy pandas matplotlib SciPy and optimization/ML libraries
• Strong research engineering: clean code reproducible experiments versioning and production readiness
• Hands-on experience developing Relative Value strategies
• Experience building systematic strategies in equities / futures / options / other listed derivatives (any strong TradFi systematic experience is relevant)
• Good knowledge of option maths and strong options intuition
• Familiarity with common quant tooling (e.g. QuantLib and/or in-house libraries)

Nice to Have

• Experience with execution-aware modeling and/or close collaboration with execution / low-latency teams (HFT exposure a plus)
• Position-driven surface shaping  adapting surface/spread to current portfolio Greeks
• Practical experience applying ML/DL (PyTorch TensorFlow LightGBM) in trading with careful validation and overfitting controls
• Experience trading exchange-margined derivatives where capital efficiency is a first-order constraint (NSE options CME Eurex)  comfortable optimizing return-on-margin under SPAN-style portfolio margining
• Direct experience in cross-instrument arbitrage (spot / futures / options)

Additional Information :

What we offer:

• Experience a modern international technology company without the burden of bureaucracy.
• 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.

Remote Work :

Yes

Employment Type :

Full-time