Asset Management- Quantitative Equities Research Analyst - Vice President

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Asset & Wealth Management

Quantitative Equity Researcher (VP) at JPMorgan Chase focused on developing novel alpha signals and enhancing return-forecasting models using advanced statistical, econometric, and machine learning techniques, including LLMs and generative AI. The role involves applying these techniques to large datasets, designing research pipelines, building research frameworks, and collaborating with portfolio managers and technology teams to integrate research into production systems. Requires strong Python and SQL skills, and experience with NLP and alternative data for repeatable research.

What you'd actually do

  1. Develop novel alpha signals from traditional and alternative datasets and enhance return-forecasting models for equity markets.
  2. Apply advanced statistical, econometric, and machine learning techniques to large and complex datasets.
  3. Leverage large language models and generative AI to develop quantitative signals and generate investment insights.
  4. Design and implement robust research pipelines with proper validation methodology, disciplined feature selection, and rigorous model evaluation.
  5. Build and maintain research frameworks for factor analysis, signal diagnostics, and regression-based studies.

Skills

Required

  • 5+ years of experience in quantitative equity research or a related field
  • Advanced degree (Masters or PhD) in financial engineering, data science, computer science, mathematics, statistics, or related quantitative discipline
  • Proficiency in AI/ML fundamentals for financial applications, with practical experience across classical and modern modeling approaches
  • Strong knowledge of large language model technologies and their practical application to research and financial analysis workflows
  • Experience with NLP and working with alternative and unstructured data in a manner suitable for repeatable research and production deployment
  • Deep expertise in quantitative modeling, portfolio construction, and equity markets
  • Strong programming skills in Python, including the ability to build robust research infrastructure and reusable analytics
  • SQL proficiency for data extraction and manipulation
  • Excellent verbal and written communication skills
  • Proven ability to manage multiple research workstreams and deliver results in a fast-paced environment
  • strong prioritization
  • disciplined documentation
  • Demonstrated ability to collaborate effectively across investment, research, and technology teams

What the JD emphasized

  • independent research delivery
  • project leadership
  • practical application to research and financial analysis workflows
  • repeatable research and production deployment
  • rigorous model evaluation
  • disciplined documentation

Other signals

  • Leverage large language models and generative AI to develop quantitative signals and generate investment insights.
  • Apply advanced statistical, econometric, and machine learning techniques to large and complex datasets.
  • Develop novel alpha signals from traditional and alternative datasets and enhance return-forecasting models for equity markets.