Quantitative Research - Public Finance - Associate or Vice President

If you are passionate, curious and ready to make an impact, we are looking for you.

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 Credit QR team is responsible for developing and maintaining models for valuation, risk, PL calculations, as well as quoting and market making algorithms and analysis tools for the Global Spread business. The responsibilities of the team span the full range from new model specification, going through model approval, ensuring compliance with internal policies and industry regulations, implementation of model in library, to integration into risk and PL systems.

The Credit Quantitative Research team is looking for an experienced Quant to join in a role which will focus on covering the Public Finance business.

The opportunity is to join our London team, with a focus on pricing models and quoting infrastructure for the Public Finance team which covers mainly activity in the Municipal bond trading business. While the role spans all aspects of QR coverage, it will initially focus on enhancing our suggested price algo capabilities.

The role requires the combination of very strong software development skills, a very structured mathematical approach to problem solving, business overview, and the ability to work in a dynamic environment. This role is also expected to interact a lot with other sub-teams within Credit QR, for example the Credit Data & Algo team, as well as the Securities and Financing team. This means that a good attitude as a team player is an absolute must.

Job Responsibilities:

• Developing and improving models for the pricing and risk management of municipal bonds and their derivatives, as well as covering Muni Bonds ETFs
• Liaising with business functions as well, understanding their requirements, write project documents and steer the research in the Muni Bond suggested price algo.
• Writing model documentation compliant with internal and regulatory standards
• Working with model control teams to facilitate timely and efficient review and approval of models
• Explaining model behavior, carrying out scenario analyses, developing and delivering quantitative tools, and supporting analytics

Required qualifications, capabilities, and skills:

• You have an advanced degree in math, statistics, physics, financial engineering, computer science
• You have at least 2 years of experience supporting bond market making desk
• You demonstrate an exceptional analytical, quantitative and problem-solving skills
• You demonstrate a knowledge of quantitative modelling and risk neutral pricing
• You possess a knowledge of fixed income markets, in particular credit products and models
• You have a prior experience and knowledge with corporate or muni bonds, ETFs and algo pricing techniques
• You demonstrate strong software design and data science skills, preferably with some C++ and Python knowledge and experience
• You demonstrate excellent communication skills (required in our interaction with trading, technology, and control functions)

Preferred qualifications, capabilities, and skills:

• You demonstrate a pro-active attitude: should have a natural interest to learn about our business, models, and infrastructure
• You demonstrate ability to work in a high-pressure environment
• You demonstrate attention to detail and focus on quality of deliverable
• You have Ph.D. in a numerate subject from a top academic institution