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CIB QR - Quantitative Research - Systematic Trading - Vice President

Req #: 180018813
Location: London, ENG, UK
Job Category: Accounting/Finance/Audit/Risk
Job Description:

JP Morgan Chase 

JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world's most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Information about JPMorgan Chase & Co. is available at


Our Firmwide Risk Function 

Our Firmwide Risk function is focused on cultivating a stronger, unified culture that embraces a sense of personal accountability for developing the highest corporate standards in governance and controls across the firm. Business priorities are built around the need to strengthen and guard the firm from the many risks we face, financial rigor, risk discipline, fostering a transparent culture and doing the right thing in every situation. We are equally focused on nurturing talent, respecting the diverse experiences that our team of Risk professionals bring and embracing an inclusive environment.


The LOB Risk Team 

The primary aim of this team is to research and develop quantitative models for the Equity Derivatives business, as well as to ensure their compliance with internal policies and industry regulations


Quantitative skills are at the core of J.P. Morgan’s capabilities, contributing critically to the competitiveness and innovative power of our firm. The team's mission is to develop cutting-edge next generation analytics and processes to transform, automate and improve the trading operations of our Cash, Delta One and Derivatives businesses. We work closely with traders to develop data-driven solutions such as algorithmic strategies (high to low frequency), trading signals, risk models, portfolio optimization, recommendation engines, flow categorization and clustering… – and to ultimately combine them into automated trading processes.
We are seeking individuals passionate in areas such as electronic trading, machine learning, reinforcement learning, deep learning, collaborative filtering, recommender systems, optimization, computational statistics, and applied mathematics, with a keen interest to apply these techniques to financial markets and have a transformational impact on the business.
Roles and responsibilities include
  • Work closely with trading to build analytics and data-driven processes that automate and optimize trading quantitatively
  • Contribute from idea generation to production implementation: perform research, design prototype, implement analytics and strategies, support their daily usage and analyse their performance
  • Leverage on a wide range of modern techniques such as optimization (linear, quadratic, conic…), reinforcement learning, neural networks, time-series forecasting, clustering methods, dimensionality reduction methods (PCA, Kernel methods, factor models…)

Required Skills and Experience:


The ideal candidate will have:
  • Earned a MS, PhD or equivalent degree program in machine learning, mathematics, statistics, econometrics, financial engineering, computer science, operational research, physics or chemistry
  • Publications or experience in applied mathematics, statistics, optimization, computer science or data science (machine learning, reinforcement learning, computer vision, NLP…)
  • Exceptional analytical, quantitative and problem-solving skills, as well as the ability to communicate complex research in a clear and precise manner
  • Entrepreneurial spirit and passion for spreading a culture of change towards data-driven decision making
  • Strong software design and development skills using Python, C++ or Java
  • Ability to manipulate and analyze complex, large scale, high-dimensionality data from varying sources (use of TensorFlow and other standard machine learning packages)
  • Experience in finance is helpful, but not required: electronic trading, portfolio analytics (risk modelling, portfolio optimization), trading strategies (high to low frequency: market making, statistical arbitrage, option trading…), derivatives pricing and risk management
  • Autonomy, excellent communication, strong motivation and interest in electronic trading and equity markets



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