Associate - 8088712

Job Duties: Associate, Quantitative Engineering with Goldman Sachs Bank USA in Dallas, Texas. Independently design and evaluate predictive models and advanced algorithms for decision-making processes using statistical modeling and machine learning techniques. Identify relevant data sources for business needs, build and enhance data collection and query process, assess the quality of data, and source missing data points, and develop production quality code to implement and deploy to the production environment. Build advanced end-to-end machine learning models for fraud detection including data pipeline, feature engineering and full model implementation. Design proper portfolio management and hedging strategies against various market risks, including IR and FX risk. Build internal analytics and dashboards for monitoring financial and market risks and providing client insights to the leadership team. Document the modeling assumptions and methodologies, and work with the Model Risk team through model validations. Interface with the Data Architecture and Data Platform teams for model training and production processes and work closely with the TxB Finance team regarding optimization of profitability.

Job Requirements: Master’s degree (U.S. or foreign equivalent) in Mathematics, Applied Mathematics, Computer Science, Financial Engineering, or related quantitative field such as Electrical Engineering and one (1) year of experience in job offered or related role OR Bachelor’s degree (U.S. or foreign equivalent) in Mathematics, Applied Mathematics, Computer Science, Financial Engineering, or related quantitative field such as Electrical Engineering and three (3) years of experience in job offered or related role. Prior experience must include one (1) year with Master’s OR three (3) years with Bachelor’s with the following: developing probability and pricing models utilizing financial mathematics principles, including linear algebra, numerical methods, optimization, and probability; quantitative analysis and model development using advanced statistical and machine learning techniques and using common machine learning packages like pandas; coding and debugging in computational environments for risk management and scenario analysis; building and implementing financial risk models into a production environment using object-oriented programming languages such as Java, C++ or similar; full software development lifecycle, including requirements gathering, design, coding, testing, documentation, deployment, and production support; microservices architecture, REST APIs, and data analysis on large datasets using SQL, Python, or PySpark; and Sybase and other relational database management systems (RDBMS) as well as database query language.

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