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CIB QR - Quantitative Research - Credit - Corporate Bond ETF/Portfolio

Req #: 180029583
Location: New York, NY, US
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 5000 US Dollar (USD)
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.
Our business
J.P. Morgan has the leading Global Spread business in terms of volume traded, issuers traded and investor relationships. The Spread business covers Credit, SPG, and Public Finance Markets. J.P. Morgan Global Spread Trading 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. The Credit business make secondary markets in high grade bonds/CDS, high yield bonds/CDS, distressed bonds, indices, options, correlation products, and more exotic structures. The Securitized Products Group (“SPG”) engages in origination, syndicate, sales & trading, financing, and principal investments activities. Asset classes include: mortgage-backed securities, mortgage loans and consumer asset-backed securities and receivables.
Our team
The Credit QR Electronic Market Making team covers credit flow products, including corporate bonds and index products (CDX, ETFs, IBOXX derivatives, etc). We apply a scientific approach to trading and combine an understanding of credit market structure with modern data analytics to refine quoting and hedging strategies. The team is also responsible for improving traders’ workflow and creating new analytical tools.
The Credit QR EMM team is part of Credit QR, which is responsible for developing and maintaining models for valuation, risk, P&L calculations and analysis tools for the Global Spread business. The responsibilities of the team span the full range from new model research and specification, acquiring model approval, implementation of the model, to integration into production systems.
The opportunity is to join our Credit QR EMM team in New York as an Associate or VP depending on experience, with a focus on models and analytics for bond portfolios and bond ETFs. Candidates directly from university will be considered.
Key responsibilities could include:
  • Development, deployment and support of algorithms and tools for real-time pricing of corporate bond portfolios and ETFs in our in-house system
  • Research, back-testing and reporting on portfolio hedging strategies and ongoing improvements to related infrastructure
  • Applying machine learning and statistical analysis to market movements and trade data
  • Working with the trading desk to ensure optimal usage of automated strategies and analytical tools
  • Development of business intelligence tools
  • Written and verbal communication with Model Review Groups in order to make models pass strict in-house standards


We work in a very dynamic environment, and excellent communication skills are required in our interaction with trading, technology, and control functions. A healthy interest in good software design principles is essential. The role requires a detailed understanding of the corporate bond and ETF markets. It is understood that the candidate may not have this knowledge from previous experience, but the successful candidate would need to be highly motivated to gain this knowledge. A Ph.D. in a numerate subject from a top academic institution is a plus, but not an absolute requirement.

Essential skills:
  • Very strong data science background, including statistics, probability and machine learning, especially dimensionality reduction methods (component analysis, autoencoders, etc.)
  • Strong 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, Tensor Flow).
  • 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.
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