VP, Quantitative Research

PIMCO•New York, NY

15h•$205,000 - $240,000

About The Position

PIMCO is a global leader in active fixed income with deep expertise across public and private markets. We invest our clients’ capital across a range of fixed income and credit opportunities, leveraging our decades of experience navigating complex debt markets. Our flexible capital base and deep relationships with issuers have helped us become one of the world’s largest providers of traditional and nontraditional solutions for companies that need financing and investors who seek strong risk-adjusted returns. Since 1971, our people have shaped our organization through a high-performance inclusive culture, in which we celebrate diverse thinking. We invest in our people and strive to imprint our CORE values of Collaboration, Openness, Responsibility and Excellence. We believe each of us is here to help others succeed and this has led to PIMCO being recognized as an innovator, industry thought leader and trusted advisor to our clients. We are expanding our Structured Products Analytics team and seeking a highly motivated Quantitative Developer to contribute to a broad range of initiatives spanning prepayment and credit model implementation, risk analytics, and analytics platform development. This role offers the opportunity to gain deep exposure to a diverse set of structured finance instruments, including residential mortgage-backed securities (RMBS), commercial mortgage-backed securities (CMBS), and asset-backed securities (ABS). The successful candidate will join a highly collaborative analytics organization responsible for developing and maintaining a sophisticated cloud-based analytics platform that supports large-scale structured products analysis in a high-performance computing environment.

Requirements

Master's degree or higher in Computer Science, Mathematics, Financial Engineering, Statistics, Physics, or another quantitative STEM discipline. Candidates from top-tier academic programs are preferred.
3 to 5 years of hands-on software development experience in financial services, with strong proficiency in both C++ and Python.
At least 3 years of experience developing analytics or technology solutions for securitized products, including residential mortgages, commercial mortgages, and asset-backed securities.
Strong experience with the Intex CMO API and a solid understanding of structured finance deal structures, cash flow mechanics, and security-level analytics.
Knowledge of integrating prepayment and credit models with collateral-level cash flow engines and broader analytical workflows.
Experience designing and building scalable analytical systems within modern cloud computing environments.
Proven ability to operate effectively in a fast-paced development environment while maintaining a strategic perspective on platform architecture and long-term scalability.
Excellent problem-solving skills, strong attention to detail, and a commitment to developing clear, efficient, and high-quality code.

Responsibilities

Partner closely with quantitative researchers and portfolio analytics teams to design, develop, and enhance structured products analytics solutions leveraging the Intex CMO platform and mortgage market datasets, including Loan Performance and EMBS data.
Implement and maintain prepayment, credit, and cash flow models while ensuring seamless integration within a strategic enterprise analytics framework.
Develop and support analytics and risk measurement capabilities across a wide range of securitized products, contributing to both research and production environments.
Collaborate with cross-functional teams to design, build, and optimize a scalable cloud-based structured products analytics platform capable of supporting high-performance computation and large-scale data processing.
Participate in an agile development environment, balancing rapid delivery of analytical solutions with long-term platform architecture and scalability objectives.
Contribute to best practices in software engineering, testing, code quality, and production support to ensure robust and maintainable analytics infrastructure.
Use AI-powered development and analytic tools to accelerate research, code development, testing, and operation efficiency while adhering to firm standards for model governance, data security, and software quality.