Associate - 8077180

Job Duties: Associate, Quantitative Engineering with Goldman Sachs & Co. LLC in Dallas, Texas. Develop, implement, and document scenarios comprised of a broad range of economic and financial variables for businesses within the Firm. Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues. Analyze large data sets (structured and unstructured) to build predictive models of business-relevant market variables. Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, current events, statistical analysis, and programming. 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.

Job Requirements: Master’s degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative or engineering field, such as Molecular Engineering or Engineering Physics, and one (1) year of experience in job offered or a related quantitative engineering or quantitative analysis role OR Bachelor’s degree (U.S. or foreign equivalent) in Mathematics, Computer Science, Financial Engineering, Applied Mathematics, or related quantitative or engineering field, such as Molecular Engineering or Engineering Physics, and three (3) years of experience in job offered or a related quantitative engineering or quantitative analysis role. Prior experience must include one (1) year of experience (with a Master’s degree) OR three (3) years of experience (with a Bachelor’s degree) with 5 of the 7 following skills: C++, Java, or Python; optimizing code efficiency via efficient algorithmic design or parallel programming; designing comprehensive unit tests to ensure quality of code development; developing advanced stochastic simulation methods, such as Monte Carlo simulations, to gain statistical insights on complicated systems; performing quantitative analysis and model development using advanced statistical and mathematical techniques, including Bayesian analysis, time series analysis, or machine learning algorithms; implementing extension to standard data analysis or machine learning tools or libraries to integrate newly developed model into existing framework; and investigating and adapting advanced methodologies in relevant quantitative fields, such as statistics or machine learning, for model development purpose.

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