Quant ML Engineer – Time Series & Trading Insights (London)

Grahamcapitalmanagement; Greater London; Sur place; GBP 90 000 - 140 000; Plein temps

Résumé du poste

Graham Capital Management, L.P. is seeking a ML Engineer to join our Data Science team in London. You will design, prototype and implement ML solutions that feed Quantitative Research and discretionary trading teams, leveraging a blend of data, technology and analytics.

You will work closely with Portfolio Managers, Operations and Risk to translate research into scalable services. The role requires deep ML expertise, strong communication and ownership of complex projects across teams.

Qualifications

• 3+ years of hands-on experience with Machine Learning and Statistics on large, unstructured data sets.
• 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; experience deploying services.
• Experience with scientific computing, statistics, optimization, time series, panel data.

Responsabilités

• Join the Data Science team to design, prototype and implement ML solutions for quantitative research and trading.
• Work with Technology, Quant Research and Portfolio Manager groups to understand data needs.
• Develop time series models, forecasting methods, and data sources for the fund.
• Utilize state-of-the-art ML and statistics to improve alpha generation.

Connaissances

ML & Statistics

Communication

Ownership

Multi-projects coordination

Formation

Bachelor's or higher in CS/Engineering/OR

Outils

Python

C++

Spark/Scala

SQL

Docker/Containers

Description du poste

Graham Capital Management, L.P. is seeking a ML Engineer to join our Data Science team in London. You will design, prototype and implement ML solutions that feed Quantitative Research and discretionary trading teams, leveraging a blend of data, technology and analytics.

You will work closely with Portfolio Managers, Operations and Risk to translate research into scalable services. The role requires deep ML expertise, strong communication and ownership of complex projects across teams.