Global Banking & Markets-New York-Vice President, Quantitative Engineering-9134899

Goldman Sachs • New York, NY

1d • $113,000 - $276,000 • Onsite

About The Position

Goldman Sachs & Co. LLC is seeking a Vice President, Quantitative Engineering in New York, New York. This role involves collaborating with internal stakeholders to analyze user needs from a scenario design perspective and resolve issues related to data, models, and implementation. The position requires designing and implementing high-quality, scalable technology solutions using internal and open-source services. Key responsibilities include owning the full software development lifecycle from requirements gathering and user story refinement through development, testing (unit, integration, regression), User Acceptance Testing (UAT), and deployment. The role also entails analyzing large datasets (structured and unstructured) to build predictive models of market variables, building and challenging risk models, identifying and quantifying vulnerabilities across market, credit, and liquidity risk, and creating technical documentation for risk-model performance testing.

Requirements

• Master’s degree (U.S. or foreign equivalent) in Computer Science, Computer Engineering, Financial Engineering, Applied Mathematics or a related quantitative field and three (3) years of experience in job offered or a related quantitative or software engineering role OR Bachelor’s degree (U.S. or foreign equivalent) in Computer Science, Computer Engineering, Financial Engineering, Applied Mathematics or a related quantitative field and five (5) years of experience in job offered or a related quantitative or software engineering role.
• Prior experience must include three (3) years of experience (with a Master’s degree) OR five (5) years of experience (with a Bachelor’s degree) with 5 of the 7 following skills: C++, Java, or Python; quantitative analysis and model development using advanced econometric, statistical, and mathematical techniques, including Bayesian analysis, time series analysis, or machine learning algorithms; developing rigorous and scalable data management and analysis tools to provide risk oversight and support the investment process; full software development lifecycle, including requirements gathering, design, coding, testing, documentation, deployment, and production support; building multi-threaded and multi-process service-oriented enterprise applications within Unix environment; micro-services architecture design and development including REST, Spring, or other back-end technologies; and database query languages, including SQL or NoSQL technologies such as MongoDB.

Responsibilities

• Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues.
• Design and implement high-quality, scalable and thoughtful technology solutions leveraging both internal and open-source services.
• Own requirements gathering, user story refinement, development, testing (unit, integration and regression), User Acceptance Testing (UAT) and deployment.
• Analyze large data sets (structured and unstructured) to build predictive models of business-relevant market variables.
• Build and challenge risk models, identify and quantify vulnerabilities across market, credit, liquidity risk and modeling.
• Create and maintain clear and complete technical documentation of the risk-model performance testing approach and process.

What This Job Offers

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