Member of Research Staff

Voleon is a technology company that applies state-of-the-art AI and machine learning techniques to real-world problems in finance. For nearly two decades, we have led our industry and worked at the frontier of applying AI/ML to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future. Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals. As a Member of Research Staff, you will work at the forefront of modern statistical machine learning. Your research colleagues have collectively published hundreds of academic articles in top-tier venues on machine learning, systems, and theory, and we meet regularly to stay current on the latest academic research and share ideas. Founded in 2007 by two leading scientists (see management bios ), Voleon supports a culture of curiosity, collegiality, and creativity. Your work will focus on financial market prediction and portfolio optimization. The behavior of financial markets is noisy and violates a number of classical statistical assumptions, and we’ve spent over a decade pioneering scientific advances in the application of machine learning techniques to this domain. You will work with a complex and diverse array of datasets to implement and iterate on predictive models. Predicting financial markets is an enduringly hard problem, but results are immediate and unambiguous. Years of academic training has prepared you for this moment. You won’t just conduct research, you’ll apply it on a daily basis, working with a team across the entire life cycle of applied research problems. Your work will span from basic research to productizing solutions and validating their efficacy in live trading. This role is a means to make a difference: you will help direct billions of dollars in trades daily while making an enduring impact on our field. *** Relocation and work visa eligibility for qualified candidates*** 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 investment 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 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 R preferred) Ability to solve large-scale computing problems 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 “Friends of Voleon” Candidate Referral Program If you have a great candidate in mind for this role and would like to have the potential to earn $15,000 if your referred candidate is successfully hired and employed by The Voleon Group, please use this form to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . Equal Opportunity Employer The Voleon Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law. #LI-MD1