Vice President, Senior Front Office Quant – Securitized Products

Job Description

Vice President, Senior Front Office Quant | Confidential Employer

The Tone:
This is a full-time, hybrid role at RBC Capital Markets, LLC, located in New York, NY, with telecommuting permitted up to one day per week. RBC Capital Markets is a global investment bank that builds and maintains complex financial models and analytic tools for trading and risk management of securitized products and structured credit. This role is critical for developing and implementing advanced quantitative models and tools that directly support front office trading activities and ensure robust risk management.

The TL;DR
• Role: Full Time
• Type: Full-time
• Location: Hybrid – New York, NY
• Pay: $210000 yearly
• Team: Front Office Quant within RBC Capital Markets, supporting the Central Funding Group.
• Mission: To research, develop, test, and implement advanced quantitative models and analytic tools for securitized products and structured credit to support trading and risk management.
• Tech Stack: C++, Python, R, PolyPaths, Intex, EMBS, CoreLogic, SQL

What You’ll Actually Do
• Model Development: Research, develop, test, and implement securitized products and structured credit pricing, default, and loss models.
• Mortgage Modeling: Build and maintain agency and non-agency mortgage prepayment and default models, integrating them into systems like PolyPaths with Intex.
• Tool Creation: Build front office analytic tools for trading and risk, and automate analytics and reporting workflows for financial applications.
• Quantitative Analysis: Conduct data analysis, simulation, and forecasting using statistical and machine learning techniques on financial and credit-related datasets.
• System Implementation: Leverage object-oriented programming in C++, Python, and R to implement high-performance model libraries and maintain mortgage-backed securities data in databases.

The Must-Haves
• Background: Master’s degree in Financial Engineering, Quantitative Finance, Computational Finance & Risk Management or a related field, with 4 years of related work experience in a senior quantitative role.
• Experience:
* 3 years using Python for quantitative model development, data pipeline building, large-scale dataset processing, and cloud environment integration.
* 3 years using C++ to develop and maintain pricing and valuation libraries for fixed income or structured products, including unit testing and performance optimization.
* 2 years applying statistical and quantitative techniques such as regression analysis, Markov models, stochastic processes, time-series analysis, and risk simulations.
* 2 years modeling fixed income or structured products, including prepayment, credit risk modeling, and Option Adjusted Spread (OAS) – based relative value assessment.
* 1 year using SQL to query large datasets, support data pipelines, and integrate database workflows with analytics platforms.
• Skills: Quantitative modeling (securitized products, structured credit, prepayment/default), C++, Python, R (OOP), SQL, Statistical & Machine Learning (regression, Markov, time-series, classification), Financial Analysis (P&L attribution, OAS, VaR).