Analyst-Quantitative Engineering

Role Overview: Risk Engineering at Goldman Sachs is a crucial part of the risk management framework, responsible for providing analytical solutions, data-driven insights, robust metrics, and effective technologies for risk management. As a member of the GSRisk group in Risk Engineering, you will be part of a multidisciplinary team of quantitative experts focused on building the next generation of Risk pricing and analytics platform. Your role will involve designing, implementing, and maintaining the risk platform, performing quantitative analysis on large datasets, liaising with multiple groups within the firm, and mentoring junior professionals within the division. Key Responsibilities: - Design, implement, and maintain the risk platform that scales to billions of scenarios across risk stripes, financial products, asset classes, and metrics. - Design declarative domain-specific languages to express risk models across risk stripes and asset classes. - Perform quantitative analysis on large datasets to facilitate understanding of market risk for various financial derivatives, including exotic products. - Liaise with multiple groups across the firm to converge on a shared risk platform. - Communicate complex mathematical concepts clearly with internal and external stakeholders. - Mentor and develop junior risk management professionals within the division. Qualifications Required: - Bachelor's Degree in a relevant field such as Computer Science, Mathematics, Physics, or Engineering. - Strong quantitative, programming, and system design skills. - Good knowledge of statistics, system design, econometric modeling, and probability theory. - Strong written and verbal communication skills with the ability to explain complex quantitative concepts to a non-technical audience. Additional Details: Exposure to challenging problems at the intersection of mathematics, computer science, and finance, including derivative pricing, modeling market and counterparty risk, large-scale simulations, and system design challenges. Development of quantitative, programming, and system design skills, as well as product and market knowledge. Work in a dynamic teamwork environment. Preferred Qualifications: - Competence in advanced mathematics, system design, and data science. - Experience working with large data sets.,