Quantitative Engineering, Risk Economics Strats, Dallas, Associate

Goldman Sachs • Dallas, TX

2d

About The Position

Risk Engineering, 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. Risk Engineering is staffed globally with offices including Salt Lake City, Dallas, New Jersey, New York, London, Warsaw, Bengaluru, Singapore, and Tokyo. As a member of Risk Engineering, you will interface with a variety of divisions around the firm as well as the other regional offices. The interaction with numerous departments and the diverse projects that ensue allow for a challenging, varied and multi-dimensional work environment. The Risk Economics Strats (RES) team is a central part of the Goldman Sachs risk management framework with primary responsibility for: 1) developing macroeconomic and financial scenarios for firm-wide scenario-based risk management; 2) developing and implementing statistical models for credit loss forecasting, business-as-usual risk management and regulatory stress testing requirements; and 3) analyzing large datasets of risk metrics to extract valuable insights about the firm’s exposures. To fulfill these objectives, Risk Economics Strats interface with a wide array of divisional, finance and risk management groups across the firm. The cross-disciplinary nature of the projects that RES engages in makes for a challenging and multifaceted work environment. RES professionals are part of the value proposition of the firm, and we balance our key functional responsibility of control and risk management with that of being commercial. RES has strong traditions of risk management, data analytics and career development opportunities for our people.

Requirements

• Programming Languages : Proficiency in Python (preferred) and/or Java/Scala. Familiarity with SQL for data manipulation.
• ML/AI Frameworks : Familiar with latest LLM, RAG, agentic AI frameworks.
• Data Engineering : Strong grasp of data processing pipelines, especially with tools like SNS, SQS, Step Functions, Lambda, RDS
• APIs and Microservices : Proven experience building and consuming RESTful APIs; knowledge of FastAPI or Flask.
• Model Deployment : Familiarity with MLOps tools and patterns for deploying, monitoring, and versioning models (e.g., MLflow, SageMaker).
• Cloud Experience : Hands-on with AWS (S3, Lambda, SageMaker, RDS) or equivalent cloud platforms (Azure/GCP).
• Infrastructure as Code : Working knowledge of CI/CD pipelines, Docker, Git, and infrastructure tools like Terraform or AWS CDK.
• Credit Risk Domain Knowledge Understanding of credit scoring, risk modeling, and financial statement analysis.
• Familiarity with private and public credit data sources (e.g., CapitalIQ, Reorg, iLevel).
• Experience working with structured and unstructured financial data (e.g., PDFs, loan agreements, news articles).

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees