Equity Derivatives Quant Researcher

Goldman Lloyds; New York (NY); Hybrid; USD 150,000 - 260,000; Full time

Job summary

Goldman Lloyds in New York is seeking an Equity Derivatives Quant Researcher to work across systematic options and volatility research in a hybrid research and development role. You will collaborate with researchers and traders, backtest ideas, implement production strategies, and build high-performance Python tools for research, testing, and risk analysis in a fast-paced hedge fund environment.

Candidates bring strong quantitative experience in equity derivatives, pricing, hedging, and

Qualifications

• Strong quantitative experience in equity derivatives, options, volatility.

Responsibilities

• Research and backtest systematic equity options and volatility strategies.
• Develop high-performance Python research and backtesting frameworks.
• Research volatility risk premia, relative-value, volatility forecasting and systematic derivatives opportunities.
• Build analytics around implied volatility surfaces, term structures, Greeks, hedging and options risk.
• Develop and evaluate trading signals using statistical, quantitative and machine-learning techniques.
• Analyse strategy performance, transaction costs, P&L attribution, drawdowns and risk.
• Translate successful research into robust production strategies and tooling.
• Work directly with investment professionals on new research ideas and improvements to existing strategies.

Skills

Equity derivatives

Options

Volatility

Python

Backtesting

Statistics

Quantitative research

Index options

Education

Master's degree

PhD

Tools

Research platforms

Backtesting engines

Quant trading infra

Job description

A leading hedge fund is looking to hire an Equity Derivatives Quant Researcher to work across systematic options and volatility research.

This is a hybrid research and quantitative development seat for someone who wants to remain highly technical while having greater opportunity to originate, test and develop investment ideas.

You'll work closely with researchers and traders across the full lifecycle of a strategy — from initial research and backtesting through implementation, hedging, risk analysis and production.

The Role

• Research and backtest systematic equity options and volatility strategies.
• Develop and enhance high-performance Python research and backtesting frameworks.
• Research volatility risk premia, relative-value, volatility forecasting and systematic derivatives opportunities.
• Build analytics around implied volatility surfaces, term structures, Greeks, hedging and options risk.
• Develop and evaluate trading signals using statistical, quantitative and machine-learning techniques.
• Analyse strategy performance, transaction costs, P&L attribution, drawdowns and risk.
• Translate successful research into robust production strategies and tooling.
• Work directly with investment professionals on new research ideas and improvements to existing strategies.

What We're Looking For

• Strong quantitative experience within equity derivatives, options, volatility or systematic equity strategies.
• Deep understanding of options, volatility, derivatives pricing and hedging.
• Strong Python with experience building research platforms, backtesting engines or quantitative trading infrastructure.
• Experience taking quantitative ideas from research through backtesting and implementation.
• Strong knowledge of statistical modelling and quantitative research techniques.
• Experience with products such as SPX, NDX, VIX or other listed/index options would be highly relevant.
• Master's or PhD in a quantitative discipline is preferred.

For someone currently sitting between QIS, derivatives quant research and quantitative development, it offers the opportunity to move closer to the investment process while continuing to build sophisticated quantitative systems.