Head of Feature Engineering

Voleon is a technology company that applies state-of-the-art AI and machine learning techniques to real-world problems in finance. For more than a decade, 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.

We are seeking a senior researcher to lead feature engineering for predictive models. You will lead a team of researchers in applying financial insight and statistical machine-learning methods to uncover predictive signals from structured and unstructured data. Your work will directly improve model performance and investment outcomes.

This is a unique opportunity to set a feature research agenda, mentor outstanding colleagues, and collaborate with best-in-class teams across research, engineering, and investment strategy.

Responsibilities

• Lead the design and development of features for use in supervised and unsupervised learning, drawing on a variety of econometric and financial concepts.
• Translate economic and financial model constructs into scalable, empirical signal definitions, using structured and unstructured data.
• Collaborate with data engineering teams to implement clean, robust pipelines for feature generation and version control across large securities universes.
• Shape the experimental framework for evaluating feature performance in predictive models.
• Establish and manage best practices for documenting construction logic, assumptions, and behavior of each feature under different economic and market conditions.
• Manage and coach the featuring engineering team, fostering a collaborative and results-driven environment.
• Align the team’s efforts with broader research and model development goals through strategic planning and cross-functional collaboration.

Requirements

• Demonstrable clarity of thought.
• Grit, in large quantities.
• 7+ years of experience in quantitative finance, signal research, or predictive modeling, including at least 2 years leading research teams or initiatives.
• Strong scientific programming skills in Python and/or R; experience with SQL or Spark is a plus.
• Deep understanding of financial markets, company fundamentals, econometrics, and financial-economic models.
• Extensive experience designing features for use in machine-learning pipelines or research environments (e.g., PyTorch, scikit-learn).
• Master's degree in a relevant quantitative field.

Preferred

• Ph.D. in a quantitative field.
• Experience managing globally distributed research or data-analysis teams.
• Familiarity with alternative data.

The base salary range for this position is $250,000 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 formto 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.