About J.P. Morgan J.P. Morgan is a leader in financial services, working in collaboration across the globe to deliver the best solutions and advice to meet our clients' needs, anywhere in the world. We operate in 150 countries, and hold leadership positions across our businesses. We have an exceptional team of employees who work hard to do the right thing for our clients and the firm, every day. This is why we are the most respected financial institution in the world - and why we can offer you an outstanding career.
J.P. Morgan Global Credit Trading business is a global leading market maker in bonds, credit derivatives, exotic products and solutions. It offers first-class, highly integrated financial services to a global client base and provides financial assets and liquidity for banks, insurance companies, finance companies, mutual funds and hedge funds. Traders, salespeople and research analysts work collectively to generate ideas and provide markets to clients.
The Credit QR Electronic Market Making team works closely with traders to cover Credit flow products, including Corporate Bonds and Index products. We apply a scientific approach to trading by combining an understanding of market microstructure with modern data analytics to develop quoting, market-making and hedging strategies. The team is also responsible for improving traders' workflow and creating new analytical tools.
The Credit QR EMM team is a part of Spread QR, which is responsible for developing and maintaining models for valuation, risk, P&L calculations and analysis tools for the Credit, SPG and Public Finance businesses. The responsibilities of the team span the full range from new model specification, applying for model approval, implementation of model in library, to integration into production systems as well as their day-to-day support.
This exciting opportunity is at the intersection of trading, data science and quantitative research. The candidate would join our Spread QR EMM team in New York as a Vice President, working as part of the Credit Trading desk to shape the future of the bond portfolio trading business. This is a unique opportunity to get exposure to trading and markets while maximally leveraging the candidate's quantitative skill set.
Key responsibilities could include:
Working with the trading desk to identify new business opportunities and to ensure optimal usage of automated strategies and analytical tools in the bond portfolio space
Research, back-testing and reporting on portfolio trading, portfolio hedging and liquidation strategies, as well as ongoing improvements to related infrastructure
Development, deployment and support of algorithms and tools for real-time pricing of corporate bond portfolios and ETFs in our in-house system
Applying machine learning and statistical analysis to market movements and trade data
Development of business intelligence tools
Written and verbal communication with Model Review Groups in order to make models pass strict in-house standards
Requirements We work in a very dynamic environment, and excellent communication skills are required in our interaction with trading, technology, and control functions. The role requires a commercial mindset, with a detailed understanding of the corporate bond and ETF markets, gained from several years of previous experience. A healthy interest in good software design principles is important. A Ph.D. in a numerate subject from a top academic institution is a plus, but not an absolute requirement.
Experience with building and using factor models for portfolio risk and return modeling.
Familiarity with fixed income analytics such as curve building and spread analytics.
Strong data science background, including statistics, probability and machine learning, especially dimensionality reduction methods (factor models, component analysis, etc.)
Good programming and OO design skills, most likely obtained using C++. In addition, Python would also be a plus as would experience with reactive programming.
Excellent practical data analytics skills on real data sets, including familiarity with methods for working with large data and tools for data analysis (pandas, numpy, scikit, TensorFlow).
Attention to detail: thorough and persistent in delivering production quality analytics.
Ability to work in a high-pressure environment.
Pro-active attitude: a self-learner, the candidate should be passionate about problem solving and should have a natural interest to learn about our business, models and infrastructure.
Internal Number: 6037759
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