Options Quant Researcher

Requirements

• 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)

Benefits

Additional Information

We're 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 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)

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Company Intel

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