Equity Derivatives Quant

Goldman Lloyds; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Goldman Lloyds, a leading hedge fund, seeks an Equity Derivatives Quant Researcher to partner with Portfolio Managers on the research, development, and implementation of options and volatility strategies.

The role blends quantitative research, backtesting, derivatives modelling, and strategy development, with opportunities to scale the firm’s equity derivatives capabilities and live investment tools.

Qualifications

• Strong background in equity derivatives, options, and volatility research.
• Experience building and backtesting quantitative strategies.
• Proficiency in Python and statistical methods for modelling.

Responsibilities

• Partner with Portfolio Managers to develop equity derivatives and options strategies.
• Build and enhance Python-based research/backtesting frameworks.
• Research opportunities across options, volatility, and systematic derivatives.
• Develop analytics for implied volatility, surfaces, Greeks, skew, and hedging.
• Design frameworks to evaluate signals, costs, P&L, risk, and performance.
• Work with large historical options datasets to identify opportunities.
• Translate research into live investment strategies and production tools.
• Contribute to expanding the firm’s options and equity derivatives platform.

Skills

Python programming

Equity derivatives

Options strategies

Backtesting frameworks

Quantitative research

Education

Master's/PhD in quant field

Job description

A leading hedge fund is seeking an Equity Derivatives Quant Researcher to work directly with Portfolio Managers on the research, development, and implementation of options and volatility strategies.

This is a highly investment-facing role combining quantitative research, backtesting, derivatives modelling, and strategy development, with significant scope to help build out the firm's broader equity derivatives capabilities.

The Role

• Partner directly with Portfolio Managers to research and develop equity derivatives and options strategies.
• Build and enhance Python-based research and backtesting frameworks for systematic derivatives strategies.
• Research opportunities across options, volatility, relative value, and systematic derivatives.
• Develop analytics around implied volatility, volatility surfaces, Greeks, skew, term structure, and hedging.
• Design robust frameworks for evaluating signals, transaction costs, P&L, risk, and strategy performance.
• Work with large historical options and market datasets to identify and validate new opportunities.
• Translate successful research into live investment strategies and production tools.
• Help drive the continued build-out of the firm's options and equity derivatives platform.

Candidate Profile

• Strong professional experience within Equity Derivatives, Options, Volatility, or systematic derivatives research.
• Experience within a hedge fund, proprietary trading firm, investment bank, or quantitative investment environment.
• Deep understanding of options pricing, volatility, Greeks, hedging, and derivatives risk.
• Strong Python and quantitative programming skills.
• Proven experience developing backtesting frameworks and quantitative research tools.
• Strong statistical and mathematical foundations.
• Experience taking strategies from research and backtesting through implementation.
• Ability to work directly with Portfolio Managers and communicate research findings in an investment context.
• Master's or PhD in Mathematics, Statistics, Financial Engineering, Computer Science, Physics, or a related quantitative discipline preferred.