Tech Stack

Description

You will work at the forefront of modern statistical machine learning, focusing on financial market prediction and portfolio optimization. Your work involves implementing and iterating on predictive models using complex datasets, spanning from basic research to productizing solutions and validating their efficacy in live trading. This role offers the chance to apply cutting-edge AI/ML techniques to real-world financial challenges in a collaborative, research-driven environment.

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

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