Vice President, Quantitative Financial Analyst

Bank of America Bank of America · Banking · New York, NY

This role focuses on quantitative analysis and model development within the Global Rates Electronic Trading Strats team at Bank of America. Responsibilities include market risk stress testing, developing trading models and electronic systems for pricing and risk management, and performing statistical analysis on large datasets. The role requires strong quantitative skills, experience in financial markets, and proficiency in Java for building production trading systems.

What you'd actually do

  1. Performs end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and analyzes stress scenario results to better understand key drivers
  2. Supports the planning related to setting quantitative work priorities in line with the bank’s overall strategy and prioritization
  3. Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on model development/validation
  4. Supports model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite
  5. Supports the methodological, analytical, and technical guidance to effectively challenge and influence the strategic direction and tactical approaches of development/validation projects and identify areas of potential risk

Skills

Required

  • Experience in designing and building production trading systems and knowledge of underlying trading infrastructure, with an emphasis on distributed low latency, high availability systems, including pricing and risk management, trade & order lifecycle management, algorithmic execution
  • Exceptional development skills in Java with experience working on multi-threaded programming and dependency injection frameworks like Google Guice or Spring
  • Academic background at undergraduate or, ideally, Masters/PhD level in a quantitative subject (Mathematics, Statistics, Physics, Engineering, Computer Science or other analytical background) or related work experience
  • Financial markets experience
  • Experience in quantitative modelling and working with large datasets

What the JD emphasized

  • production trading systems
  • distributed low latency, high availability systems
  • pricing and risk management
  • trade & order lifecycle management
  • algorithmic execution
  • Java
  • multi-threaded programming
  • quantitative subject
  • financial markets experience
  • quantitative modelling
  • large datasets