E-markets [multiple Positions Available]

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

This role focuses on applying AI/ML and quantitative techniques to options pricing, hedging, and quoting within financial markets. It involves developing and implementing sophisticated algorithms, quantitative models, and analytical tools for trading analysis and backtesting. The position requires experience with various machine learning techniques and programming languages like Python, KDB, C++, PyTorch, and Sklearn, with a strong emphasis on quantitative research and model development in a regulated financial environment.

What you'd actually do

  1. Lead the design and implementation of automated systems for options pricing, hedging, and quoting, emphasizing enhancements in speed and accuracy.
  2. Serve as technical lead for analytical tool building for both live volatility trading analysis and historical research backtesting framework.
  3. Provide sophisticated algos to help drive trading decisions with intensive research on volatility signals and applying optimization techniques.
  4. Lead automated options pricing systems for North America, expanding coverage to the Index desk with responsibilities in pricing, quoting, and model development.
  5. Provide daily desk support for operational issues.

Skills

Required

  • Artificial intelligence
  • machine learning
  • statistical methods
  • quantitative techniques for risk modeling
  • transaction cost analysis
  • volatility modeling
  • parametric implied volatility models
  • stochastic local volatility models
  • signal optimization
  • quantitative models
  • Python
  • KDB
  • C++
  • PyTorch
  • Sklearn
  • TensorFlow
  • statsmodels
  • Linear Regression
  • decision trees
  • clustering
  • neural networks
  • financial modeling
  • alternative datasets analysis
  • quantitative findings communication

What the JD emphasized

  • Ph.D. in Engineering (any), Mathematics, Statistics, Financial Engineering, Computer Science, Operation Research or related field of study plus 3 years of experience
  • Master's Degree in Engineering (any), Mathematics, Statistics, Financial Engineering, Computer Science, Operation Research or related field of study plus 5 years of experience
  • Artificial intelligence, machine learning, and statistical methods as applied to financial markets
  • Developing and implementing parametric implied volatility models and stochastic local volatility models
  • Conducting signal optimization
  • Designing and building complex quantitative models using programming languages such as Python, KDB or C++
  • Using Python, Pytorch, and Sklearn to implement machine learning algorithms, develop analytics pipelines, and support research infrastructure
  • Using machine learning techniques (including Linear Regression, decision trees, clustering, and neural networks) and programming packages (including Sklearn, TensorFlow, and statsmodels) to solve problems related to financial modeling, analyze financial and alternative datasets, and communicate quantitative findings through written reports or presentations.

Other signals

  • applying optimization techniques
  • develop analytics pipelines
  • implement machine learning algorithms