Machine Learning Engineer London, England, United Kingdom; New York, New York, United States; N[...]

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 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.

• 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 in 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.

This role requires commuting into the office Mondays through Fridays.

• 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 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.

The anticipated base salary range for this position is $175,000 to $250,000. The anticipated range is based on information as of the time this post was generated. The applicable annual base salary or hourly rate paid to a successful applicant will be determined based on multiple factors, including without limitation the nature and extent of prior experience, skills, and qualifications. Base salary or rate does not include other forms of compensation or benefits offered in connection with the advertised role.

GCM is committed to providing equal employment opportunity to all employees and applicants for employment without regard to their race, color, religious creed, gender, age, national origin, ancestry, alienage, citizenship status, handicap, disability, marital status, sexual orientation, gender identity, pregnancy, childbirth or other related conditions, military status, genetic information, or any other personal characteristics protected by applicable law. This policy applies to all terms and conditions of employment, including hiring, placement, promotion, layoff, termination, transfer, leave of absence and compensation.