Responsibilities
- Collaborate with Technical Product Owners in Research and Engineering to define priorities, scope solutions, and deliver actionable insights to stakeholders.
- Own investigations of complex production behaviors and design robust methods for identifying, analyzing, and communicating system anomalies.
- Mentor and manage junior and mid-level data scientists; provide architectural oversight for data projects.
- Serve as a technical authority within the data science function, set standards for data quality, statistical rigor, and reproducibility.
- Lead the design, development, and deployment of analytical pipelines to monitor and interpret production behavior across trading and research systems, ensuring its correctness.
- Champion best practices in data governance, tooling, and collaborative development (CI/CD, version control, code reviews).
- Drive continual growth and learning within the team by onboarding new Data Scientists, growing teams, and fostering a culture of curiosity, collaboration, and applied experimentation.
Requirements
- Master’s degree or higher in a quantitative, technical, or analytical field such as Data Science, Statistics, Computer Science, Engineering, Finance, or an MBA with strong analytical training.
- 3+ years of experience managing data scientists, including responsibility for performance development, technical direction, and project execution.
- Demonstrated ability to lead cross-functional data initiatives, translate business objectives into analytical goals, and ensure timely delivery.
- Strong understanding of data science fundamentals, including statistical analysis, data preparation, root cause investigation, and the proven ability to guide others' work in these areas.
- Familiarity with data tooling and workflows (e.g., SQL, Pandas, R, Airflow), with enough fluency to review and support technical contributors effectively.
- Basic software development skills and experience with bash, linux/unix, and git
- Experience communicating complex findings to executive and technical stakeholders, including presenting insights, tradeoffs, and recommendations.
- Track record of establishing scalable methodologies, driving technical standards, and fostering collaboration in fast-paced, analytically rigorous environments.
Preferred
- Experience building or growing high-performing data science teams in production-aware or high-stakes settings.
- Exposure to financial markets, trading systems, or quantitative research environments.
- Familiarity with production monitoring systems, data quality pipelines, or analytics platform development.
- 7+ years of experience managing data scientists.
- Experience managing a large team or multiple teams.
Top Skills
What We Do
Founded in 2007 by two machine learning scientists, The Voleon Group is a quantitative hedge fund headquartered in Berkeley, CA. We are committed to solving large-scale financial prediction problems with statistical machine learning.
The Voleon Group combines an academic research culture with an emphasis on scalable architectures to deliver technology at the forefront of investment management. Many of our employees hold doctorates in statistics, computer science, and mathematics, among other quantitative disciplines.
Voleon's CEO holds a Ph.D. in Computer Science from Stanford and previously founded and led a successful technology startup. Our Chief Investment Officer and Head of Research is Statistics faculty at UC Berkeley, where he earned his Ph.D. Voleon prides itself on cultivating an office environment that fosters creativity, collaboration, and open thinking. We are committed to excellence in all aspects of our research and operations, while maintaining a culture of intellectual curiosity and flexibility.
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.
