Job description

Who We Are The FICC and Equities Quantitative Strategists team works at the center of Goldman Sachs' global markets business. We work hand in hand with Traders to turn quantitative research into solutions directly deployed in our trading franchise, running in live markets. Our impact spans three areas: Across venues: We push the frontier of systematization and automation in our trading franchise. We research and develop market-making strategies and trading signals for better pricing and risk management. We also build low-latency quoting and execution algorithms, and the technology stack behind them, for trading on exchanges and electronic channels. On desks: We build valuation models that price derivatives, evaluate products and explain how markets behave. We create trading tools and algorithms for automated trading and hedging. We use statistical tools to measure and manage risk across asset classes. Across desks: We build and run a large-scale quoting platform and risk management infrastructure. We integrate our core pricing and risk methods, end to end, with the applications Traders use across voice and electronic trading. What We Do Operating at the center of global financial markets, Global Banking & Markets serves institutional clients including asset managers, hedge funds, banks and brokerages, pensions, endowments and foundations, corporations and governments. We help them buy and sell financial products around the world, raise funding, and manage risk. This is a dynamic, entrepreneurial team with a passion for the markets, with individuals who thrive in fast-paced, changing environments and are energized by a bustling trading floor. The Global Currencies and Emerging Markets (GCEM) Strats team works closely with Trading to develop quantitative research, models and technology to solve complex markets and risk-management problems. As a quantitative researcher within the GCEM Strats team, you will focus on Systematic Option Strategies and partner with the trading desk to research, test and implement systematic solutions to Market-Making, signal generation and enhanced risk management across global macro markets. Your Impact As a quantitative researcher, you will help shape the research agenda for Systematic Option Strategies. You will take market observations and trading hypotheses through rigorous data analysis, model development, validation, strategy implementation and live deployment. Your research will directly influence how the desk conceptualizes, prices and hedges option risk. You will develop forecasting and prediction models, signals, and systematic hedging strategies, with careful attention to transaction costs, liquidity, tail risk, capacity and robustness across market regimes. The primary focus is systematic FX options and volatility research; relevant experience may come from FX, equities, rates, commodities or another liquid macro asset class. You will work in a collaborative environment involving close interaction between Researchers and Traders while utilizing public and proprietary firm data. This is a highly visible, markets and trading-facing role with a clear path from original research to measurable commercial impact. Responsibilities As a quantitative researcher within GCEM Strats, you will drive and contribute to applied research for Systematic Option Strategies, translating market intuition and observations into robust, scalable systematic risk management and market making strategies. This includes, but is not limited to, a mandate to: • Originate and test systematic option market making approaches, incorporating a diverse array of techniques and signals. • Develop forecasting models for volatility markets, capturing skew, smile, term structure, carry effects, cross-asset relationships and market regimes. • Design and implement systematic hedging strategies for vega and related risks subject to real market constraints, balancing expected P&L, transaction costs, liquidity, basis and tail risks. • Own the end-to-end research process: idea generation, data processing, hypothesis design, feature engineering, backtesting/validation, portfolio construction and performance attribution. • Apply robust research standards, including bias controls, regime and stress analysis, sensitivity testing, realistic implementation assumptions and clear documentation. • Assess production performance of successfully deployed research and refine models as markets evolve. • Partner with Traders to gain insights into and leverage institutional knowledge of market structure and function that can inform research. • Build reusable analytical tools and contribute to shared research infrastructure, data standards and best practices. • Communicate methods, assumptions, risks and results clearly to Trading and senior stakeholders. Who We Look For We are looking for an experienced, intellectually curious quantitative researcher to join the GCEM Strats team, with strong research judgment, options intuition and a trading orientation. The ideal candidate can independently turn a market observation into a practical implementation supported by detailed quantitative analysis, while collaborating closely with Traders, fellow Strats and Engineers. Basic Qualifications and Preferred Qualifications • MS or PhD in statistics, mathematics, physics, computer science, engineering, operations research, econometrics, quantitative finance or a related quantitative discipline. • Typically 4–8 years of applied quantitative research experience in systematic options or volatility trading, systematic macro, derivatives, cross-asset relative value, statistical arbitrage or a closely related area. • Demonstrated experience developing and validating predictive models, signals, strategies or systematic hedging approaches. • Strong understanding of options, volatility and risk, including Greeks, volatility surfaces, carry and roll-down, P&L attribution, transaction costs and tail behavior. • Experience with options in FX, equities, rates, commodities or another liquid macro asset class; cross-asset experience is valued. • Excellent foundation in statistics, probability, time-series analysis, econometrics, machine learning, optimization and experimental design. • Strong programming skills and experience working with large market datasets, research pipelines and backtesting frameworks; proficiency in Python, Java, or similar languages is beneficial. • Track record of independent research ownership, sound judgment and a rigorous and diligent analytical approach. • Clear written and verbal communication skills, intellectual honesty, high standards and the ability to work effectively in a fast-paced collaborative environment. Salary Range The expected base salary for this New York, NY, United States-based position is $155000-$300000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end. Benefits Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non-temporary, full-time and part-time US employees who work at least 20 hours per week, can be found here .

Requirements

Must-haves

This includes, but is not limited to, a mandate to:
Originate and test systematic option market making approaches, incorporating a diverse array of techniques and signals.
Develop forecasting models for volatility markets, capturing skew, smile, term structure, carry effects, cross-asset relationships and market regimes.
Design and implement systematic hedging strategies for vega and related risks subject to real market constraints, balancing expected P&L, transaction costs, liquidity, basis and tail risks.
Own the end-to-end research process: idea generation, data processing, hypothesis design, feature engineering, backtesting/validation, portfolio construction and performance attribution.
Apply robust research standards, including bias controls, regime and stress analysis, sensitivity testing, realistic implementation assumptions and clear documentation.
Assess production performance of successfully deployed research and refine models as markets evolve.
Partner with Traders to gain insights into and leverage institutional knowledge of market structure and function that can inform research.
Build reusable analytical tools and contribute to shared research infrastructure, data standards and best practices.
Communicate methods, assumptions, risks and results clearly to Trading and senior stakeholders.
Who We Look For
MS or PhD in statistics, mathematics, physics, computer science, engineering, operations research, econometrics, quantitative finance or a related quantitative discipline.
Typically 4–8 years of applied quantitative research experience in systematic options or volatility trading, systematic macro, derivatives, cross-asset relative value, statistical arbitrage or a closely related area.
Demonstrated experience developing and validating predictive models, signals, strategies or systematic hedging approaches.
Strong understanding of options, volatility and risk, including Greeks, volatility surfaces, carry and roll-down, P&L attribution, transaction costs and tail behavior.
Experience with options in FX, equities, rates, commodities or another liquid macro asset class; cross-asset experience is valued.
Excellent foundation in statistics, probability, time-series analysis, econometrics, machine learning, optimization and experimental design.
Strong programming skills and experience working with large market datasets, research pipelines and backtesting frameworks; proficiency in Python, Java, or similar languages is beneficial.
Track record of independent research ownership, sound judgment and a rigorous and diligent analytical approach.
Clear written and verbal communication skills, intellectual honesty, high standards and the ability to work effectively in a fast-paced collaborative environment.

Nice-to-haves

Basic Qualifications and Preferred Qualifications
The expected base salary for this New York, NY, United States-based position is $155000-$300000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.

Application prep

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Keywords to mirror

machine learningpythonjava

Platform: goldman-sachs
Effort: medium

Benefits

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Wellness