Member of Research Staff, Causal Inference, Voleon Securities
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 54d ago
The date the source published, not the day we noticed it (2026-08-17). Last seen at its source 2h ago.
We have tracked this listing since 5 Oct 2026 (5 days). The employer's own board has carried it every time we have read it, most recently 2 hours ago.
Is it remote?
Remote, United States
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $117k–192.4k/yr
Middle 50% of 391 listings that do state pay — Operations · Senior · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Ashby employer's own board first seen 5d ago · last seen 2h ago
The listing
Voleon Securities, a new business within the Voleon Group, provides liquidity in securities markets. We apply state-of-the-art AI/ML techniques to construct our liquidity-provision strategies. For more than a decade, our affiliate Voleon Capital Management has led the hedge fund industry and worked at the frontier of applying AI/ML to investment management, becoming a multibillion-dollar asset manager. Voleon Securities builds on Voleon’s deep real-world experience applying ML to financial markets.
We are looking to add an experienced and creative causal inference researcher to our growing ML research group. We welcome researchers with both strong theoretical foundations and experience applying causal inference methods in industrial settings. Financial applications of causal inference are challenging and demand new methods that go beyond the academic state-of-the-art; it is critical candidates have a deep understanding of the field to draw inspiration for new methodology.
This is a chance to join the initial buildout of a fully modern securities business rooted in the frontier of AI/ML and statistics. 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 across Voleon 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, 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.
Relocation and work visa eligibility for qualified candidates
Responsibilities
Develop a rich understanding of Voleon’s challenges and methodologies and propose causal inference research innovations and experiments to build, maintain and optimize models of the market
Prepare and analyze new market datasets to gain insight into market microstructure
Develop, validate, and implement improvements to our models of the market
Design and conduct synthetic and live trading experiments to sharpen understanding of market behavior
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 causal inference academic research to identify novel approaches to explore for application to our domain
Requirements
Ph.D. level coursework is required, and a Ph.D. degree in a relevant field is preferred
Background in causal inference and statistics with a strong track record of publishing causal inference papers in top tier journals and conferences
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)
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
“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.