Member of Research Staff, Causal Inference
Conducts causal inference research for financial market prediction and portfolio optimization, developing and validating models from research through live trading. Requires Ph.D.-level coursework, strong causal inference and statistics expertise, mathematical ability, and production Python skills.
About the job
Responsibilities
- Develop a rich understanding of Voleon’s challenges and methodologies and propose causal inference research innovations and experiments to build, maintain, and optimize market models.
- Prepare and analyze new market datasets to gain insight into market microstructure.
- Develop, validate, and implement improvements to market models.
- Design and conduct synthetic and live trading experiments to improve understanding of market behavior.
- Communicate and collaborate with research staff and software engineers throughout the applied research lifecycle, driving progress toward tangible outcomes.
- Keep up to date on causal inference research to identify novel approaches for application to financial markets.
Requirements
- Ph.D.-level coursework is required; a Ph.D. in a relevant field is preferred.
- Background in causal inference and statistics, with a strong record of publishing causal inference papers in top-tier journals and conferences.
- Strong mathematical abilities demonstrated through publications, graduate coursework, or competition placement.
- Interest in software development techniques and willingness to write production-level Python code.
- Eagerness to work in a fast-paced, growing business.
- Essential interest in financial applications; prior finance industry experience is not required.
Compensation
- Referral bonus of up to $15,000 may be available through the Friends of Voleon candidate referral program, subject to program eligibility and terms.
Skills
Causal Inference, Statistics, Machine Learning, Python, Mathematical Modeling, Market Microstructure, Portfolio Optimization, Predictive Modeling, Synthetic Experiments, Live Trading
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