Lead Quant — Market Microstructure & Securities Lending

Voleon is a technology company that applies state-of-the-art 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 machine learning to investment management. We have become a multi-billion-dollar asset manager, and we have ambitious goals for the future.

Your colleagues will include internationally recognized experts in machine learning research as well as highly experienced finance and technology professionals. You will be working with strategies that are at the forefront of machine learning and statistical trading. The strategies have been carefully designed to generate non-correlated returns. Our firm and its strategies are the product of many years of meticulous research and craftsmanship, and you will lead the way in stewarding them for the future.

As a Lead Quant, you will use deep markets knowledge alongside quantitative skills to lead a team tasked with improving the implementation of systematic trading strategies. Domains include improving algorithmic execution, securities lending, and portfolio financing across a variety of asset classes and markets. You will report directly to the Head of Trading, and work at the intersection of trading and research on problems that require market domain expertise but also statistical and quantitative rigor.

Why this role

• Lead a high‑impact team improving how our ML‑driven strategies meet real markets.
• Tackle market problems across microstructure, algorithmic execution, securities lending, and portfolio financing.
• Collaborate across Trading and R&D; see your research make its way into production.
• Competitive pay and benefits; remote‑friendly with a Berkeley hub.

What you’ll do

• Lead and mentor a small team of Quantitative Trading Strategists and Data Scientists.
• Research market questions across asset classes; create clear analyses and present findings across teams.
• Build production‑quality code (Python/R/SQL): packages, data pipelines, and trading applications.
• Partner with Trading & Research to improve strategy implementation and market interaction.
• Provide domain expertise in market microstructure to improve algorithmic execution.
• Evaluate order placement and liquidity access (e.g., venue usage, routing, and tactics) as part of broader market implementation.

What we’re looking for

• 5+ years in a quantitative trading environment.
• Strong programming in Python, R, and SQL; you write production code and work well across teams.
• Applied statistics, including hypothesis testing and causal inference.
• Clear quantitative reasoning.
• Deep market microstructure knowledge and passion for markets; excellent communication.

Nice to have

• Experience with securities lending / equity finance / portfolio financing (e.g., stock‑loan, TRS, repo), and/or TCA/market‑impact analysis.
• Leadership experience.

The base salary range for this position is $200,000 to $300,000 in the location(s) of this posting. Individual salaries are determined through a variety of factors, including, but not limited to, education, experience, knowledge, skills, and geography. Base salary does not include other forms of total compensation such as bonus compensation and other benefits. Our benefits package includes medical, dental and vision coverage, life and AD&D insurance, 20 days of paid time off, 9 sick days, and a 401(k) plan with a company match.

“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.

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