Risk Engineering, Vice President, Market… at Goldman Sachs · New York

Risk Engineering (“RE”), which is part of the Risk Division, is a central part of the Goldman Sachs risk management framework, with primary responsibility to provide robust metrics, data-driven insights, and effective technologies for risk management. RE is staffed globally with offices including Dallas, New Jersey, New York, Salt Lake City, London, Warsaw, Bengaluru, Singapore, and Tokyo.

In Risk Strats, we are a team of quantitative experts responsible for driving timely and efficient risk management across the firm's market, credit, liquidity, and capital exposures. We design and deliver quantitative models, metrics, tools, and analyses essential to the firm's financial control and reporting functions.

The team is responsible for designing, implementing and maintaining quantitative measures of risk such as Value at Risk, Exposure Modelling, Stress Testing, as well as metrics used to determine the firm's capital requirements, along with tools and systems that streamline and bring efficiency to the review and explanation of these metrics by leveraging Large Language Models (LLMs).

Whether assessing the creditworthiness of the firm’s counterparties, monitoring market, credit, and liquidity risks associated with trading activities, or offering analytical and regulatory compliance support, our work contributes directly to the firm’s success. The division is ideal for collaborative individuals who have strong ethics and attention to detail. FUNCTIONALITY DESCRIPTION

• Developing risk models, generating risk metrics and sensitivity analysis that use advanced mathematical/statistical/engineering approaches such as optimization, stochastic calculus, machine learning
• Performing detailed analysis on risk trends and drivers and communicating insights to internal and external stakeholders, harnessing the latest advancements in Large Language Models (LLMs) to deliver timely, efficient, and high-quality analysis, review, and explanation.
• Strong commercial mindset, with a proven ability to deliver pragmatic, working solutions quickly and iterate based on stakeholder feedback and changing priorities.
• Updating and maintaining risk models along with business growth and risk environment changes
• Developing and maintaining large scale risk infrastructures/systems using strong programming experience in at least one compiled or scripting language (e.g. C, C++, Java, Python, Scala)
• Experience in designing highly scalable, efficient and robust systems
• Effectively communicating results and outputs from the model and insights from analysis

QUALIFICATIONS

• Post graduate degree /bachelor’s degree in mathematics, Physics, Electrical Engineering or related technical discipline
• Passion for financial markets with strong familiarity across asset classes (equities, rates, credit, derivatives) and how they drive risk and capital.
• Quantitative engineering mindset — blends financial intuition with rigorous methods to build robust, production-ready analytics.
• Hands-on experience building LLM agents over financial data (RAG, tool-use, agentic workflows) to automate analysis and explanation.
• Commercial mindset — applies software and LLMs to solve real financial problems, optimizing for impact over technology.
• Strong analytical and problem-solving skills using math, statistics, and programming
• Demonstrated ability to learn new technologies and apply
• Excellent communication skills including experience speaking to technical and business audiences and working globally
• Strong programming experience in at least one compiled or scripting language (e.g. C, C++, Java, Python)

Salary Range

The expected base salary for this New York, New York, United States-based position is $130000-$250000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.

Benefits

Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non-temporary, full-time and part-time US employees who work at least 20 hours per week, can be found here.