Member of Research Staff, Voleon Securities (New York)
Develops and deploys advanced ML models for financial market prediction and portfolio optimization in a market-making strategy. Requires PhD-level expertise in statistical ML, preferably deep RL, deep learning, optimal control, or causal inference, with strong math and Python skills.
About the job
Responsibilities
- Develop a rich understanding of Voleon’s challenges and methodologies and propose research innovations and experiments to build, maintain and optimize the models that govern our trading strategy
- Prepare and analyze new datasets to assess their predictive efficacy
- 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 statistical methods and machine learning with a track record as an applied researcher, preferably with experience in at least one of the following: optimal control, deep RL, deep learning, and causal inference
- Hands-on experience building successful liquidity providing strategies across asset classes preferred but not required
- 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)
- Ability to solve large-scale computing problems
- Eagerness to work in a fast paced and growing business
- 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
Skills
Machine Learning, Deep Learning, Deep Rl, Optimal Control, Causal Inference, Python, Statistical Methods, Financial Market Prediction, Portfolio Optimization, Large-Scale Computing
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