Machine Learning Engineer

Graham Capital Management, L.P. (collectively with its affiliates, "Graham") is an alternative investment manager founded in 1994 by Kenneth G. Tropin. Specializing in discretionary and quantitative macro strategies, Graham is dedicated to delivering strong, uncorrelated returns across a wide range of market environments. As one of the industry’s longest-standing global macro and trend-following managers, Graham remains committed to innovation, evolving its strategies through a robust investment, technology, and operational infrastructure. Graham harnesses the synergies between its discretionary and quantitative trading businesses to offer a broad suite of complementary alpha strategies, each built on the principles of thoughtful portfolio construction, active risk management, and diversification by design. Graham invests significant proprietary capital alongside its clients – including global institutions, endowments, foundations, family offices, sovereign wealth funds, investment management advisors, and qualified individual investors – reinforcing alignment of interests across all strategies. The foundation of Graham’s sustainability and success is the experience and contributions of its people. The firm seeks to cultivate talent, encourage the diversity of ideas, and respect the contributions of all. In turn, each employee shares in the responsibility of strengthening thos [... source excerpt omitted ...] oad spectrum of financial and alternative data. Our objective is to support the research process by providing our stakeholders with all the right pieces to succeed in their jobs. Responsibilities You will be part of a growing team within Data Science. You will work alongside world-class talent to find innovative solutions to some of the most interesting problems on the buy-side. You will work closely with other areas such as Technology, Quantitative Research and Portfolio Manager groups as well as Risk and Operations to learn about problems they face with respect to data and ultimately develop cutting edge solutions. Your focus will be to dive deep into multiple data sets to underst [... source excerpt omitted ...] support quant strategies, and provide new insights and leverage state-of-the-art machine learning and advanced statistical methods to produce the best data sources for the fund. Requirements Undergraduate or higher degree in Computer Science, Engineering, Operations Research, or other quantitative discipline 3+ years of hands-on experience with Machine Learning and Statistics on large, unstructured, data sets Experience writing production code for multi-client systems serving model results is a great plus Ability to clearly communicate research findings to technical and nontechnical stakeholders Full-stack experience with Python (preferred) or C++, Spark/Scala, SQL or other di