Quantitative Modeler, Associate - Securitized Products Modeling Team

BlackRock
New York, NYPosted 2 April 2026

Tech Stack

Job Description

About this role We are seeking an Associate Quantitative Modeler to join the team within Aladdin Financial Engineering (AFE) - Single Security . The team is responsible for the development, enhancement, and governance of models and analytics across a broad range of securitized products, including Agency MBS, Non A gency RMBS, CRT, MSR, CMBS /CRE , ABS /ABF , and CLOs , delivered on the Aladdin platform to internal portfolio teams and external clients. This role is ideal for a technically strong quantitative professional who enjoys research and building models, working with large datasets, and partnering closely with investment, risk, technology, and client teams to deliver high q uality analytics at scale. Key Responsibilities Develop , enhance, and maintain securitized products models using econometric and machine learning /AI techniques. Collaborate with Data, Technology, Portfolio Management, Client, and Risk teams to ensure analytics meet business and client needs. Clearly communicate modeling frameworks, assumptions, and results to both technical and non technical audiences ; s upport client discussions by explaining complex analytics and modeling concepts in a clear, intuitive, and practical manner. P erform rigorous validation of model inputs and outputs across development, implementation, and production environments. P lay a key role in model risk management, controls, and regulatory compliance; own model performance monitoring frameworks and engage with governance forums. Identify opportunities to automate, streamline, and scale analytical and reporting processes across the modeling platform, enhancing monitoring and reporting workflows through the creative design of tools and applications, including modern AI enabled technologies. Qualifications 3-4 years of hands-on experience in quantitative model development and i mplementation . Strong quantitative background with a d egree in Computer Science , Engineering, Mathematics, Statistics, Economics , or a related field . Proficient programming skills in Python , R ( or equivalent ) ; C familiarity is a plus. Strong communication skills and ability to explain complex analytics clearly. Experience with large datasets, productio n level analytics, or financial modeling platforms is a plus . Knowledge of securitized products is a plus but not required . 
For New York, NY Only the salary range for this position is USD$137,500.00 - USD$170,000.00 . Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits. BlackRock operates a pay-for-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance.
 Our benefits To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about. Our hybrid work model BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock. About BlackRock At BlackRock, we are all connected by one mission: to help more and more people experience financial well-be ... (truncated, view full listing at source)
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