Quantitative Strategist, Global Banking & Marke...

<p><strong><u>What We Do</u></strong></p> <p>At Goldman Sachs, quantitative strategists are the cutting edge of our businesses, solving real-world problems through a variety of analytical methods. Working in close collaboration with traders and sales, strats invaluable quantitative perspectives on complex financial and technical challenges power our business decisions.</p> <p>Within our Global Banking & Markets business, <strong>Goldman Sachs Electronic Trading (GSET)</strong> has launched an initiative to become the top provider in Electronic US Listed Options Trading by building superior technology and delivering high quality products. This vision is a multi-year investment in people, platforms, research, and products. Join our team and participate in the research that powers best-in-class products for top clients across the industry.</p> <p> </p> <p><strong><u>Who We Look For</u></strong></p> <p>We are looking for eager, nimble, and ambitious quantitative strategists to join our growing team of visionaries, and drive Goldman Sachs Electronic Trading to achieve and exceed our goals. The ideal candidate thrives on complex challenges, possesses strong mathematical and analytical skills, and is eager to conduct deep dives into financial problems. Our team works with modern Python frameworks and uses KDB databases. Within our team, you will have ample opportunities to be curious, to solve problems for our clients, and directly impact the success of our business.</p> <p> </p> <p><strong><u>Responsibilities</u></strong></p> <ul> <li>Design, build, and maintain high-performance options trading algorithms for our clients.</li> <li>Apply machine learning techniques across a variety of large datasets in order to build strong predictive models.</li> <li>Use Trade Cost Analysis (TCA) to monitor, analyze, and improve the execution of options trading algorithms.</li> <li>Communicate with traders, sales, clients, and compliance officers about new features and models.</li> </ul> <p> </p> <p><strong><u>Qualifications</u></strong></p> <ul> <li>Advanced degree in a quantitative field.</li> <li>5+ years experience with Python and/or KDB in a research or machine learning context.</li> <li>Experience in data-driven trading performance analysis and optimizations is preferred.</li> <li>Strong communication skills and the ability to work as part of a team.</li> </ul> <p> </p> <p><u>Salary Range</u></p> <p>The expected base salary for this New York, New York, United States-based position is $150k - $300k. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.</p> <p><u>About Goldman Sachs</u></p> <p>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. </p> <p>We believe who you are makes you better at what you do. Were 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. </p> <p>We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: <a href="https://www.goldmansachs.com/careers/footer/disability-statement.html" target="_blank">https://www.goldmansachs.com/careers/footer/disability-statement.html</a></p> <p>© The Goldman Sachs Group, Inc., 2025. All rights reserved.</p>GSET