at J.P. Morgan
Bulge Bracket Investment BanksPosted 3 days ago
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**Quantitative Trading & Research Analyst - RMBS: Drive Decisions with Python Skills** As our Quantitative Trading & Research Analyst, you'll strategically support our top-ranked RMBS underwriting desk. In this role, you'll leverage strong Python skills to build and maintain tools, driving data-driven decisions across underwriting, structuring, banking, trading, and risk teams. **Key Responsibilities:** - **- Data Analysis:** Manipulate large datasets to create systematic, repeatable outputs. - **- Tool Development:** Build and enhance analytics tools to support RMBS deal execution. - **- Collaborate:** Work with stakeholders to integrate tools and ensure robust, maintainable solutions. **Required Skills & Experience:** - **Education:** Bachelor's/master's degree in a quantitative discipline. - **Programming:** Proficient Python skills (required). - **Quantitative Foundation:** Strong numerical skills and interest in financial markets. - **Communication:** Excellent communication skills, collaborating effectively with diverse teams. ** Preferred Qualifications:** - **SQL knowledge, financial markets experience, and RMBS familiarity** for faster onboarding.
- Compensation
- Not specified USD
- City
- New York City
- Country
- United States
Currency: $ (USD)
Full Job Description
Location: New York, NY, United States
Position Summary
Join our dynamic team as a Desk Strategist supporting a market-leading RMBS underwriting business. As an Analyst within Quantitative Trading and Research (QTR), you will work closely with underwriting, structuring, financing, banking, trading, risk, and technology to build cutting-edge analytics and tooling that shape how the business evaluates collateral, prices risk, sources clients, and executes transactions.
As an Analyst on the Quantitative Trading & Research Team, you will sits at the intersection of quantitative research, modern engineering, and front-office Residential Mortgage-Backed Securities (RMBS) deal execution, offering direct exposure to senior underwriting, structuring, banking and trading partners on complex transactions. You are a hands-on builder with strong Python skills and a genuine interest in financial markets and will be a primary asset in shaping the team's future analytics and workflows by delivering scalable, production-quality tools and analytical/pricing solutions that improve decision-making, drive profitability, and support optimal deal execution.
Job Responsibilities
Manipulate large datasets to produce systematic analyses and repeatable outputs delivered directly to the desk.
Develop tools and quantitative analyses to drive decision making related to RMBS underwriting, pricing, structuring, and securitization deal execution
Build and enhance scalable desk tooling that improves the efficiency, reliability, and usability of analytics and models in a live deal environment.
Assist in the development and maintenance of internal valuation, scenario analysis, and risk frameworks as applicable.
Collaborate with technology and internal teams to integrate tools with desk systems and data platforms; ensure solutions are robust and maintainable.
Apply strong engineering discipline (testing, documentation, version control) and leverage firm-approved AI-assisted development where appropriate to accelerate delivery.
Required Qualifications, Capabilities, and Skills
Bachelor's or master's degree in a quantitative discipline (computer science, mathematics, statistics, engineering, physics, economics/finance, or related).
Excellent programming skills (Python required).
Strong quantitative foundation and interest in financial markets.
Excellent communication skills and ability to partner effectively with technical and non-technical stakeholders.
Self-motivated with strong critical thinking, ownership, and attention to detail.
Preferred Qualifications, Capabilities, and Skills
SQL knowledge is a plus.
Prior financial markets experience is helpful.
Prior mortgage / structured products knowledge is helpful.
Experience building reusable tools used by others (not just one-off analysis).
