Asset & Wealth Management, Quantitative Investment Strategies, Liquid Alternatives, Analyst / Associate - New York

Who We Are

Goldman Sachs Asset Management is one of the world's leading investment managers, providing institutional and individual investors with investment and advisory solutions across asset classes, industries and geographies. We help our clients navigate dynamic markets and identify the opportunities that shape their portfolios and long-term investment goals. We extend these global capabilities to the world's leading pension plans, sovereign wealth funds, central banks, insurance companies, financial institutions, endowments, foundations, individuals and family offices.

The Quantitative Investment Strategies (QIS) group is a market leader in quantitative portfolio management, using advanced quantitative methods to structure, manage and monitor investment portfolios. Within QIS, the Liquid Alternatives team delivers hedge fund beta strategies, systematic multi-asset alternative strategies and options-based solutions to investors.

The Role

This Associate role sits within QIS Liquid Alternatives and focuses on our hedge fund and private asset return tracker strategies, contributing to the research, innovation and future growth of the franchise. You will work on systematically replicating the return drivers of private and alternative assets in liquid, investable form.

The opportunity pairs junior and senior professionals on the same problems, giving the candidate direct exposure to experienced portfolio managers and researchers as you learn and apply advanced financial and econometric methods.

Responsibilities

• Conduct quantitative research on hedge fund and private asset return data to identify replicable return drivers.
• Help develop cross-asset systematic trading strategies that fit within the broader factor tracking portfolio.
• Work with large structured and unstructured datasets to extract insights that inform systematic investment strategies.
• Contribute to the development, enhancement and maintenance of the research and portfolio management platform, spanning strategy backtesting, performance monitoring and risk analysis.

Basic Qualifications

• Bachelor's, Master's or PhD in Financial Engineering, Statistics, Mathematics, Physics, Computer Science or a related quantitative discipline.
• 0–3 years of relevant experience (including internships).
• Strong foundation in statistics, linear algebra and calculus.
• Strong programming skills (Python, Matlab, R, C++ or other languages).
• Excellent analytical and quantitative skills, with keen attention to detail.
• Ability to work effectively in a collaborative, fast-paced, team-oriented environment.

Preferred Qualifications

• Exposure to machine learning techniques applied to financial or time-series data.
• Familiarity with systematic investing, factor models or alternative asset classes.
• Experience building or maintaining research infrastructure (version control, testing, data pipelines).
• Intellectual curiosity and a demonstrated ability to approach problems creatively.