Lead Data Scientist
BrigitFull Time
Expert & Leadership (9+ years)
Candidates must possess a Master's degree or higher in a quantitative field such as Data Science, Statistics, Computer Science, Engineering, or Finance, or an MBA with strong analytical training. A minimum of 3 years of experience managing data scientists, including performance development, technical direction, and project execution, is required. Strong understanding of data science fundamentals, including statistical analysis and data preparation, is essential. Familiarity with data tooling and workflows like SQL, Pandas, and R, along with basic software development skills in bash, Linux/Unix, and Git, is necessary. Experience communicating complex findings to executive and technical stakeholders is also a requirement. Preferred qualifications include experience building or growing high-performing data science teams, exposure to financial markets or trading systems, familiarity with production monitoring systems, 7+ years of experience managing data scientists, and experience managing a large team or multiple teams.
The Data Scientist, Technical Lead will provide technical direction and management for a team of data scientists working on trading and research systems. Responsibilities include shaping analytical frameworks, ensuring methodological rigor, and delivering insights to inform business and research decisions. They will collaborate with stakeholders in engineering, research, and leadership to drive innovation and excellence. Key duties involve defining priorities, scoping solutions, owning investigations of complex production behaviors, designing methods for analyzing system anomalies, and mentoring junior data scientists. The role also entails setting standards for data quality, statistical rigor, and reproducibility, leading the design and deployment of analytical pipelines, championing best practices in data governance and tooling, and fostering a culture of curiosity and collaboration within the team.
Investment management using machine learning algorithms
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.