Quantitative Trading & Research - Credit - Vice President

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Commercial & Investment Bank

Seeking a VP to join Credit Quantitative Trading team, leveraging AI/ML to enhance trading strategies and decision-making. The role involves exploring and integrating AI/ML methodologies, developing and deploying models end-to-end in production, and ensuring robustness and scalability. Requires experience in quantitative research, fixed income markets, and AI/ML timeseries models.

What you'd actually do

  1. Investing in existing AI/ML models and continuously improving capabilities to keep up with modern frameworks
  2. Collaborate with market makers, traders, and other stakeholders to support trading activities and strategies.
  3. Analyze market trends and large datasets, translating them into actionable insights using various methodologies relevant to the projects.
  4. Design and implement tools and systems end-to-end, ensuring that models and analytics comply with industry best practices.
  5. Foster a deep understanding of how different parts of the business connect and contribute to the overall success of both the individual and the team.

Skills

Required

  • post-graduate degree in a STEM discipline
  • hands-on experience in AI/ML timeseries models
  • traditional statistical modeling
  • quantitative research
  • e-trading space
  • corporate credit
  • fixed income markets
  • ETFs
  • analytical skills
  • systematic approach to problem-solving
  • independent thinking
  • organizational capabilities
  • communication skills

Nice to have

  • deep learning
  • time series modeling
  • Python
  • Java
  • libraries
  • micro-services
  • systems

What the JD emphasized

  • end-to-end
  • AI/ML

Other signals

  • AI/ML to enhance trading strategies
  • develop and deploy proven results in a production setting end-to-end
  • AI/ML models are robust, scalable
  • investing in existing AI/ML models and continuously improving capabilities
  • deep learning, time series modeling