Team Lead, Scientific Data Workflow Automation
TetraScienceSalary not specified
Full Time
Expert & Leadership (9+ years)
Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Finance, Investment ManagementIndustries
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, Git), with enough fluency to review and support technical contributors effectively
- 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
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
Skills
AI
Machine Learning
Data Science
Analytical Pipelines
Statistical Rigor
Data Quality
Reproducibility
Trading Systems
Research Systems
The Voleon Group
Investment management using machine learning algorithms
About The Voleon Group
Voleon focuses on investment management by utilizing machine learning to analyze financial market trends. The firm uses advanced statistical models to process large datasets and identify patterns that inform investment decisions, setting it apart from traditional methods that rely on human intuition. Voleon serves institutional clients and operates on a performance-based fee structure, aligning its interests with those of its clients. The company's goal is to provide data-driven insights that optimize investment returns while adapting to changing market conditions.
Berkeley, CaliforniaHeadquarters
2007Year Founded
VENTURE_UNKNOWNCompany Stage
Quantitative Finance, Financial ServicesIndustries
51-200Employees
Benefits
Health Insurance
Dental Insurance
Vision Insurance
Life Insurance
Paid Vacation
Paid Sick Leave
401(k) Retirement Plan
401(k) Company Match
Risks
Competition from other quantitative hedge funds may erode Voleon's market share.
Regulatory scrutiny on AI use in finance could increase compliance costs for Voleon.
Data quality issues could lead to inaccurate predictions and financial losses for Voleon.
Differentiation
Voleon uses machine learning for data-driven financial market predictions.
The firm serves institutional clients with a focus on scalability and risk management.
Voleon's academic approach emphasizes intellectual rigor and continuous learning.
Upsides
Increased interest in ESG investing offers new opportunities for Voleon's models.
Alternative data sources enhance predictive models for quantitative hedge funds like Voleon.
Cloud computing enables efficient scaling of Voleon's data processing capabilities.
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