Asset & Wealth Management- New York- Associate, Quantitative Internal Product Specialists

Job Duties Associate, Quantitative Internal Product Specialists with Goldman Sachs & Co. LLC in New York, New York. Serve as a product expert on Quantitative Investment Strategies, Alternative Investments, develop new quantitative investment ideas based on research, market structure and statistical analysis. Support existing funds and help launch new ones, supporting both internal and external clients of the Division and the Firm. Write code in Python, Java, C or SQL to solve analytical problems and produce analysis on markets and investment strategies. Create client‑specific proposals and analysis tailored to address the unique needs of individual investors. Develop content and analysis on Markets, Quantitative Investment technique. Develop performance reporting and ongoing risk contribution and performance attribution analysis across asset portfolios. Support advisors, salespeople, and clients to understand account lifecycle events. Collaborate with teams across the division to drive the commercial success of the business. Prepare methodology documentation and marketing material and complex due diligence questionnaires for the QIS funds. Develop strong working relationships across our business, working closely with portfolio managers, strategists, and engineers to gain insight into our investment process, as well as compliance, legal, controllers, operations. Collaborate with our investment team, sales and marketing to create marketing collateral and determine the best vehicle for delivery (webinar, blog posts, white paper, etc.). Serve as a point of contact for advisors and sophisticated institutional clients regarding the Firm's Alternative Investment strategies, investment processes, and performance through client meetings and call. Mentor junior members of the team and help manage and prioritize responsibilities and deliverables. Job Requirements Master's degree (U.S. or foreign equivalent) in Financial Engineering, Mathematics, Applied Mathematics, Computer Science, or related field and one (1) year of experience in the job offered or in a related role OR Bachelor's degree (U.S. or foreign equivalent) in Financial Engineering, Mathematics, Applied Mathematics, Computer Science, or related field and three (3) years of experience in the job offered or in a related role. Prior experience must include one (1) year of experience (with a Master's degree) or three (3) years of experience (with a Bachelor's degree) with the following: working in financial markets and investment, sales and trading teams with understanding of structured investment products, and option strategies; utilizing programming languages to perform quantitative analysis, automate reports, materials, and/or processes, specifically advanced Python or C++ experience. Development, production implementation, and maintenance of software; utilizing PowerPoint and Excel to create and update proposals and/or marketing materials; computing risk and performance analytics on structured investment products; derivatives, Option knowledge, Volatility Regimes Modeling and portfolio construction; pricing and structuring multi-asset financial derivatives. Statistical and stochastic modelling of financial products for prediction and risk management; principal component analysis, hypothesis testing, linear/non-linear optimization. Time series analysis and large dataset manipulation; and communicating complex investment concepts to non-technical audience. FINRA Series 7TO, 63 required. 10% domestic and international travel required to conduct client presentations in person. All travel costs will be reimbursed by the firm. Salary Range Annual base salary for this New York, New York based position is $191,922. Equal Opportunity Employer 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, veteran status, disability, or any other characteristic protected by applicable law. #J-18808-Ljbffr