Member of Research Staff, Optimization
Conduct optimization research and implement large-scale constrained optimization models that drive real-time trading decisions, working across the full research lifecycle from theory to production. Requires PhD-level coursework and strong applied research background in optimization.
About the job
Responsibilities
- Develop a rich understanding of Voleon’s challenges and methodologies and propose research innovations and experiments to build, maintain and improve the models that govern our investment strategy
- Design, implement, and improve large-scale constrained optimization methods that determine trading decisions under forecasts, risk, costs, liquidity, and operational constraints
- Develop, validate, and implement new models into production
- Design and conduct experiments to improve simulations and evaluate the success of new models in a live environment
- Communicate and collaborate effectively with other Members of Research Staff and Software Engineers at each stage, driving progress towards tangible outcomes
- Keep up to date on the latest academic research to identify novel approaches to explore for application to our domain
Requirements
- Background in modern optimization techniques and algorithms with a track record as an applied researcher
- Evidence of strong mathematical abilities (e.g., publication record, graduate coursework, or competition placement)
- Interest in software development techniques and willingness to write production level code (Python and/or C++ preferred)
- Familiarity with numerical methods, optimization solvers, mathematical programming, convex or nonconvex optimization
- Eagerness to work in collaborative and diverse teams
- Interest in financial applications is essential, but prior finance industry experience is not a pre-requisite
- Ph.D. level coursework is required, and a Ph.D. degree in a relevant field is preferred
Preferred Qualifications
- Expertise in stochastic control, and reinforcement learning
Skills
Python, C++, Numerical Methods, Optimization Solvers, Mathematical Programming, Convex Optimization, Nonconvex Optimization, Stochastic Control, Reinforcement Learning, Large-Scale Optimization
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