Machine Learning Engineer
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About the Role
Graham Capital Management, L.P. ("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 those around them.
Description
Graham Capital Management, L.P. is seeking a ML Engineer to join our Data Science team, a future-looking technical arm of Graham Capital. We envision, design, prototype and implement the processes that feed Quantitative Research and Discretionary Trading teams as well as the broader firm. We are passionate about what we do and welcome every opportunity to prove it.
The Data Science department straddles traditional Data Science and Engineering roles as well as the application of Machine Learning & AI. We work closely with Quant Researchers, Portfolio Managers, Operations and Execution to continuously improve upon our offering. Every day we work to transform our business through data, technology, and the insights we provide our stakeholders.
At Graham Capital, our systems feed live models around the clock, span billions of market data ticks, an ever-increasing corpus of news and other texts as well as a broad 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 understand relationships, develop time series, forecasting models, and 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
- 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 distributed data processing technologies as well as experience working comfortably building and deploying services and models in containerized environments
- Experience with scientific computing, statistics, optimization, time series, panel data, etc.
- Comfortable handling multiple projects to solve varied problems working with multiple teams
- Detail-oriented mindset
- Sense of ownership of his/her work, working well both independently as well as collaboratively
This role requires commuting into our London office Mondays through Fridays.
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