Global Banking & Markets - New York - Associate, Quantitative Engineering - 4195191

Goldman Sachs • New York, NY

21h • $113,000 - $155,600 • Onsite

About The Position

Goldman Sachs Services LLC is seeking an Associate, Quantitative Engineering in New York, New York, to join their Applied AI Team. This Artificial Intelligence (AI) Quantitative role focuses on deploying AI-based quantitative technologies to drive revenue generation and innovation within the firm. The position requires leveraging advanced knowledge in computer science, statistics, artificial intelligence, and machine learning to tackle unique challenges at the intersection of Quantitative Finance and AI. Responsibilities include collaborating with various teams on projects integrating AI with quantitative finance, such as Time Series Forecasting, Market making, and Pricing. The role involves addressing specific challenges of applying these techniques to the financial sector, pushing the state-of-the-art in AI for Quantitative Finance, designing, training, and deploying scalable AI models for commercial outcomes, conducting experiments to enhance model performance, collaborating on the production of machine learning systems, and developing, testing, and maintaining high-quality, production-ready code.

Requirements

• Master’s degree (U.S. or foreign equivalent) in Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field and one (1) year of experience in job offered or a related role OR Bachelor’s degree (U.S. or foreign equivalent) in Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field and three (3) years of experience in job offered or a related role.
• One (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with: Working in an Artificial Intelligence (AI) Quantitative role.
• One (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with: C++, Java or Python programming language.
• One (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with: data structures, algorithms, and software engineering practices.
• One (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with: machine Learning, Deep Learning, Large Language Models, and Time Series Forecasting algorithms.
• One (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with: ML libraries and frameworks, including TensorFlow, PyTorch, scikit-learn, and Keras.
• One (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with: big Data Technologies and MLOps tools such as Kubeflow or MLflow in production.
• One (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with: Financial markets, market making, or asset pricing.

Responsibilities

• Deploy AI-based quantitative technologies to drive revenue generation and innovation within the firm.
• Leverage advanced knowledge in computer science, statistics, artificial intelligence, and machine learning to address unique challenges and redefine possibilities at the intersection of Quantitative Finance and AI.
• Collaborate with various teams and divisions on pioneering projects that integrate artificial intelligence with quantitative finance, such as Time Series Forecasting, Market making, and Pricing.
• Address the specific challenges that arise when applying these techniques to the financial sector and push the state-of-the-art in AI for Quantitative Finance.
• Design, train, and deploy scalable AI models to drive commercial outcomes.
• Conduct experiments and analysis to enhance model performance.
• Collaborate effectively with colleagues to advance the production of machine learning systems and applications.
• Develop, test, and maintain high-quality, production-ready code.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level