Machine Learning Research Engineers are responsible for developing novel models and applying them to large-scale problems. They work together closely with Data Engineers and Software Engineers. Goldman Sachs has a culture where everyone gets a lot of responsibility from day one. Researchers enjoy a relatively large degree of freedom, and can work on self-initiated projects as well as on project ideas that originate from the revenue side. We encourage researchers to develop relationships with researchers at partner universities and to contribute to the field through publications.
At Goldman Sachs, we have a wide array of problems that relatively few machine learning researchers are looking at. Researchers get to work on problems and challenges unique to the financial industry. Externally, we are students of the markets, and internally we develop products for our clients and internal teams. At any time, our team has a portfolio of many projects to work on. General topics of interest to us are time series modelling, probabilistic machine learning, forecasting, deep learning, and natural language processing. This means that researchers can work on a wide variety of projects, across different business units. Researchers at Goldman Sachs are encouraged to be thought leaders and shape our vision for data science.
Researchers will be based in the Execution Services business units, which work on problems on the entire execution stack, from trading algorithms to sales. The team is part of Securities strats, and is primarily responsible for execution research, alpha-signal generation, market microstructure studies, transaction cost modelling (TCM) and other quantitative investigations of interest to both the agency and principal execution desks.
Responsibilities & Qualifications
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