Senior Member of Research Staff, Technical Lead (London)

Overview

As a Senior Member of Research Staff and Tech Lead, you will lead cutting-edge statistical ML research for financial market prediction and portfolio optimization, guiding a research team from concept to production. You will collaborate with globally recognized experts and speak for rigorous scientific standards while shaping the research agenda. This role offers high leverage to tackle hard, real-world problems and to contribute to Voleon’s mission in finance through advanced AI. You will mentor peers and drive impact across research and engineering teams.

Responsibilities

• Lead original research on predictive models, new data sets, risk management, and portfolio optimization
• Develop deep domain understanding of Voleon’s challenges and methods
• Define and oversee the research agenda and project execution
• Maintain high standards of scientific rigor, efficiency, and correctness
• Mentor and guide other Research staff
• Communicate with stakeholders and discuss complex issues
• Stay current with academic research to identify novel methods
• Collaborate with engineering to productionize successful research
• Contribute to recruiting top talent

Key requirements

• 3+ years directing research projects and mentoring colleagues
• Experience growing research teams in a collaborative environment
• Expertise in modern statistical methods, machine learning, and their domain applications
• Strong mathematical foundations (publications, coursework, or competitions)
• Clear, concise communication of complex technical topics
• Ability to make reasoned decisions, anticipate outcomes, and form/validate hypotheses
• Capability to manage multiple projects and operate large-scale compute infrastructure
• Proclivity for prototyping numerical software
• Interest in financial applications (prior finance experience not required)
• Ph.D. level coursework required; Ph.D. degree preferred

• clear communication
• mentorship
• collaborative teamwork
• modern statistical methods
• machine learning
• large-scale compute infrastructure

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