Quantitative Developer - Equity Derivatives
Full-time
Goldman Lloyds
Quantitative Developer – Equity Derivatives | Front Office - New York
Financial Services Firm
Hybrid on-site
The role combines Python engineering, quantitative analytics and equity derivatives trading knowledge, working directly with a senior trader to develop tools for evaluating trading strategies and investment performance.
This is an opportunity for a technically strong Quant Developer with established Equity Derivatives business knowledge who is interested in gradually expanding into Quantitative Research and strategy development.
Key Responsibilities
- Design and develop backtesting frameworks supporting equity options and derivatives trading strategies.
- Build scalable research infrastructure for historical simulations, strategy evaluation and performance analysis.
- Develop Python-based tools for analysing options strategies, volatility, Greeks, hedging and trading P&L.
- Work with historical market data, options chains, volatility surfaces and derivatives datasets.
- Collaborate directly with Quantitative Researchers and Traders to implement and evaluate investment ideas.
- Improve the accuracy, scalability and computational performance of research and simulation platforms.
- Develop analytical frameworks supporting strategy attribution, transaction costs, risk-adjusted returns and portfolio behaviour.
- Contribute to quantitative research initiatives as the role evolves.
Requirements
- 5+ years of experience in Quantitative Development, Front Office Technology or Quantitative Trading Systems.
- Essential: Strong professional knowledge of Equity Derivatives, including options, volatility, Greeks and trading workflows.
- Advanced Python programming skills, with experience building production-quality research applications and analytical frameworks.
- Experience developing backtesting engines, trading simulations or quantitative research infrastructure.
- Strong understanding of algorithms, data structures, statistical analysis and financial time-series data.
- Familiarity with derivatives market data, historical datasets and strategy performance evaluation.
- Strong academic background in Computer Science, Mathematics, Physics, Financial Engineering or a related quantitative discipline.
- Ability to collaborate directly with Portfolio Managers, Traders and Quantitative Researchers.
#QuantitativeDeveloper #PythonEngineering #FactorModels #RiskTechnology #QuantitativeRisk #HedgeFund #PortfolioAnalytics #DistributedSystems
Vacancy posted 2 days ago
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