Quantitative Volatility Trading, Goldman Sachs' electronic market-making team, is seeking a Quantitative Researcher to help grow its on-exchange options trading business in Hong Kong.

This role is suited to a researcher with a solid track record in derivatives modeling and automated trading who combines strong mathematical intuition with a practical, data-driven mindset. You will focus on the research, design, and calibration of automated pricing, quoting, and risk-management models in high-throughput electronic markets. Working closely with traders, quant researchers, and engineering partners, you will drive continuous strategy improvement across listed options markets while leveraging Goldman Sachs' global scale and liquidity franchise.

Responsibilities

• Research, calibrate, and enhance mathematical models for real-time options pricing and dynamic volatility surfaces
• Extract predictive signals and statistical patterns from tick-level market data and limit order book dynamics
• Design, backtest, and refine automated quoting and execution algorithms
• Build simulation tools, backtests, and diagnostics to evaluate strategy performance, inventory risk, and execution slippage
• Partner closely with traders to translate live market observations and microstructure phenomena into algorithmic improvements
• Work with quantitative developers and software engineers to transition research prototypes into high-performance production code
• Evaluate expansion opportunities across products, markets and geographies
• Contribute to a disciplined, collaborative research culture focused on rigorous experimentation and measurable trading impact

Skills and Experience

• 5+ years of experience in quantitative research within an options market-making, electronic trading, or systematic derivatives environment
• Strong foundation in options pricing theory, volatility dynamics, Greeks risk management, and order book microstructure
• Proven ability to apply rigorous statistical and mathematical methods to signal research, pricing, or execution problems
• Strong programming skills in Python for statistical analysis, data manipulation, and strategy backtesting
• Practical working proficiency in modern C++ for latency-sensitive execution and production modeling
• Experience conducting research on large-scale, high-dimensional tick-by-tick and order-event datasets
• Sound engineering and quantitative judgment balancing mathematical elegance with live implementation realities
• Strong communication skills and a collaborative approach to partnering across trading, research, and core engineering
• Advanced degree (Masters or Ph.D. preferred, or Bachelors with demonstrable experience) in Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline

ABOUT GOLDMAN SACHS

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.  

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.  

Were committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

The Goldman Sachs Group, Inc., 2026. All rights reserved.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.