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CIB QR – Quantitative Research Inventory Optimisation – Vice President

Req #: 170093130
Location: London, ENG, UK
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 3500 Pound Sterling (GBP)
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.
Business Area
JPMorgan Investor Services Quantitative Research team is looking for a strong quant to support all quantitative aspects of the Equity Finance & Delta 1 businesses.
The team is responsible for developing and maintaining models for valuation, risk, P&L calculations as well as balance sheet and inventory optimization.
Specifically, the responsibilities of the team include new model specification, going through model approval, model implementation, as well as integration into production systems.
Opportunity to join our London team as an Associate, with a focus on Inventory Optimization.
Key responsibilities include:
  • Lead forward the CIB-wide inventory optimization initiative, aimed to streamline cross-business inventory exchanges.
  • Put in place large and scalable architectures, linearize state-space to deal with massive data sets, vectorised coding and distributed computing.
  • Liaise with multiple stake-holders to formulate a multi-dimensional objective function across metrics: PnL, Liquidity, RWA, ROA, etc.
  • Model inventory dynamics, transaction costs potentially employing Statistical and Machine Learning techniques.
  • Contribute to the continuous development of the firm-wide optimization framework.
  • Document and test new/existing models with traders and with other various control groups, such as the Model Review Group
  • Implementation of models in C++ and Python proprietary libraries.
  • Work closely with technology on integration of optimization models with front end applications
  • Ongoing desk support
The role requires a combination of a very structured mathematical approach to problem solving, experience with quantitative modelling and the ability to work in a dynamic environment.
Excellent communication skills are essential in our interaction with trading, technology, and control functions.  A healthy interest in good software design principles is a requirement as well.
A Ph.D. in a numerate subject or computer science from a top academic institution is a plus, but not an absolute requirement. Minimum requirement: Master’s degree or equivalent in said quantitative field.
Essential skills:
  • Excellent Math background
  • Ability to work with big-data and experience in formulation of optimizations.
  • Strong experience in linearization of state-space.
  • Strong analytical and problem solving abilities.
  • Strong communication/presentation skills.
  • Strong programming skills (C++, Python).
  • Experience with parallel computing, vectorization and memory management is positively regarded.
  • Solid knowledge with CPLEX, GUROBI, MOSEK or other main stream optimization packages is desirable.
  • Knowledge of Financial Engineering, Secured Financing and Prime is a plus.
  • Previous experience with formulation of Statistical models / hands-on implementation of Machine Learning neural networks is a plus.
  • Advanced degree in a technical field from a top-tier school/program: engineering, sciences, computer science, applied math.
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