VP, Quantitative Volatility Trading–Electronic Options (HK)

Goldman Sachs; Hong Kong; On-site; HKD 1,200,000 - 1,900,000; Full time

Benefits offered by this job

Healthcare Insurance

Vacation Policy

Financial Wellness

Job summary

Goldman Sachs in Hong Kong seeks a Quantitative Volatility Trading Researcher to advance its electronic options market making. The role focuses on pricing, quoting, and risk models in high-throughput environments, working with traders, researchers and engineers to push strategy improvements across listed options markets.

The ideal candidate has strong mathematical intuition, a solid derivatives background, and hands-on coding in Python and C++.

Qualifications

• 5+ years of experience in quantitative research within options market-making or electronic trading.
• Strong foundation in options pricing, volatility dynamics, Greeks risk management, and order book microstructure.

Responsibilities

• Research, calibrate and enhance mathematical models for real-time options pricing and dynamic volatility surfaces.
• Extract predictive signals from tick-level data and LOB dynamics.
• Design, backtest and refine automated quoting and execution algorithms.
• Build simulation tools to evaluate strategy performance, inventory risk and execution slippage.
• Collaborate with traders, researchers and engineers to translate observations into improvements.
• Help transition research prototypes into production code with engineers.

Skills

Python for analysis

C++ for latency

Quantitative research

Options pricing theory

Market microstructure

Tick data analysis

Backtesting

Education

Master's or PhD preferred

Bachelor's with demonstrable experience

Tools

Python

C++

Job description

Goldman Sachs in Hong Kong seeks a Quantitative Volatility Trading Researcher to advance its electronic options market making. The role focuses on pricing, quoting, and risk models in high-throughput environments, working with traders, researchers and engineers to push strategy improvements across listed options markets.

The ideal candidate has strong mathematical intuition, a solid derivatives background, and hands-on coding in Python and C++.