Senior Data Scientist — Investment Management Fintech Strategies (IMFS)

Senior Data Scientist - Investment Management Fintech Strategies (IMFS)

Investment Management Fintech Strategies (IMFS) is a global team headquartered in Malvern, Pennsylvania. We partner with investment teams to explore and apply new technologies that can improve investment performance, strengthen decision-making, and help capital markets work better for Vanguard's investors. Our work spans advanced analytics and AI/ML, new data sources, market structure and liquidity research, and building scalable platforms and tools that move from experimentation into production.

Responsibilities:

• Own end-to-end research using large-scale historical data: form hypotheses, run analyses, develop models, and translate results into insights that can improve portfolio construction and trading decisions.
• Identify and solve market-impact problems across microstructure, fund flows, and liquidity dynamics-surfacing risks and opportunities tied to execution quality and market impact.
• Design and lead the evolution of core research tooling, including the back-testing framework and signal libraries, to enable repeatable, high-quality experimentation.
• Apply AI/ML thoughtfully in financial markets, staying current on emerging techniques and evaluating what is practical, robust, and scalable in real-world market environments.
• Raise the bar on engineering quality by mentoring quant strategists and data scientists on production-grade code, data pipelines, and research-to-production best practices.
• Partner with technology to integrate models into production systems, monitor live performance, and iterate based on market feedback and measured outcomes.
• Represent Vanguard externally by contributing to industry discussions and thought leadership in areas relevant to AI/ML, systematic research, trading, and market structure.

• 5+ years leading or driving advanced data science work, including back-testing, simulation, and statistical modeling in complex problem spaces.
• Master's or PhD in machine learning, data science, financial engineering, computer science, or related quantitative discipline.
• Strong end-to-end data science capability: problem framing → research design → modeling → validation → deployment partnership.
• Proficiency in Python and common ML libraries; experience collaborating with data engineering and bringing models into production.
• Demonstrated ability to apply AI/ML to solve complex technical problems through collaboration, creativity, and disciplined research.
• Comfortable partnering with senior leaders-communicating tradeoffs, impact, and value in a clear, decision-oriented way.