Job Specifications
We are seeking a highly skilled Quantitative Researcher to join a leading financial services firm. You’ll be working directly with a Developer to build out an options market making desk.
Job Responsibilities
Lead the research and development of advanced volatility models for options market making across any asset class (equities, commodities, FX, rates, crypto, etc.), including custom implied volatility surfaces, term structures, skew/smile dynamics, and stochastic/local/rough volatility frameworks.
Design, calibrate, and backtest options pricing engines, risk models, and market-making strategies, incorporating inventory management, hedging costs, flow dynamics, and real-time P&L attribution.
Analyze vast datasets of options chains, order flow, market microstructure, and cross-asset signals to uncover trading edges and refine quoting, skew, and volatility arbitrage approaches.
Collaborate directly with traders, Quant Developers, and senior leadership to translate research into live production strategies, optimizing for liquidity provision and risk-adjusted returns.
Build and maintain internal libraries for volatility forecasting, scenario analysis, and stress testing, ensuring models are robust under volatile market conditions.
Monitor academic and industry advancements in options MM, volatility trading, and machine learning applications, piloting innovative techniques to maintain a competitive edge.
Mentor junior researchers and contribute to the firm-wide quant culture, driving data-driven decision-making for the new desk.
Required Qualifications, Capabilities, and Skills
PhD or MSc in Quantitative Finance, Mathematics, Physics, Statistics, or a related quantitative discipline.
4+ years of hands-on experience in options research, preferably in market making, prop trading, or volatility desks at a hedge fund, HFT firm, or bank.
Deep expertise in building custom volatility models (SABR, Heston, SVI, GARCH variants, rough volatility) from first principles, including surface fitting, calibration, and dynamic evolution.
Strong proficiency in Python (pandas, NumPy, SciPy, scikit-learn) for data analysis, modeling, and simulation; C++, Rust, or Julia experience a plus for performance.
Proven track record in options market making concepts: implied vs. realized vol, Greeks hedging, inventory risk, and cross-asset correlations.
Solid understanding of statistical methods, time-series analysis, and ML techniques applied to financial markets.
Excellent communication skills to convey complex models to traders and stakeholders.
Preferred Qualifications, Capabilities, and Skills
Experience with options in commodities or energy markets (futures options, spreads, etc.).
Hands-on with high-frequency data pipelines, backtesting frameworks (e.g., Zipline, Backtrader), or cloud-based simulation.
Publications, open-source contributions, or conference presentations in volatility modeling or options MM.
Familiarity with regulatory aspects of derivatives trading (MiFID II, EMIR).