Portfolio Risk Quantitative Modeler, Associate - Aladdin Financial Engineering

BlackRock•New York, NY

6d•$137,500 - $170,000•Hybrid

About The Position

BlackRock is a global asset management firm providing investment, risk management, and advisory services. The Aladdin Financial Engineering (AFE) team is responsible for the research and development of quantitative financial and behavioral models and tools across various asset classes and areas, including pricing, risk, optimization, and simulations. AFE also manages the technology platform that delivers these models. This role is for a Quantitative Associate within the Portfolio Risk team in AFE, focusing on quantitative research, model development, testing, and implementation. The position requires deep engagement with data and code to build practical, production-ready risk models and analytics. The Portfolio Risk team develops and maintains analytics such as multi-factor Linear risk models, Value-at-Risk (VaR) methodologies, volatility and covariance matrix estimation, and portfolio stress testing and scenario analysis. These models are critical for investment and risk management decisions on the Aladdin platform. The role also involves contributing to the team's AI transformation journey by applying AI and automation to modernize and scale model governance workflows.

Requirements

Master’s degree (e.g., MFE) or PhD in a quantitative field such as Finance, Economics, Mathematics, Statistics, Computer Science, or Engineering
Strong hands-on programming experience, primarily in Python (R a plus)
Experience working with large datasets and applying statistical, econometric, or quantitative techniques
Solid understanding of financial markets, financial products, and basic economics
Strong analytical and problem-solving skills with high attention to detail
Clear written and verbal communication skills in English
Ability to work effectively in a collaborative, team-oriented environment
Critical thinking and intellectual curiosity
Strong ownership of work and accountability for quality
Ability to translate complex quantitative ideas into practical, usable solutions
Comfort working across disciplines (quant research, engineering, risk, product)
Interest in building robust, scalable, and well-governed analytical systems
Innovative thinking balanced with sound judgment and practicality

Nice To Haves

Exposure to machine learning and AI techniques, particularly as applied to financial or time-series data
Experience applying AI, ML, or automation to model lifecycle and governance workflows, such as validation, back-testing, testing, monitoring, documentation, or code migration
Knowledge of fixed income and/or equity risk factor models
Understanding of portfolio theory and risk analytics
Experience designing rigorous testing and back-testing frameworks
Familiarity with building scalable and repeatable research or modeling processes
Strong software engineering practices (clean, well-tested code)
Experience with Unix/Linux and Git

Responsibilities

Research, design, and back-test portfolio risk models using Python-based infrastructure
Work hands-on with large and complex financial datasets, ensuring data quality and robustness of results
Collaborate closely with software engineers to test, productionize, and maintain models
Support existing models in production, including investigation and resolution of model-related questions from internal stakeholders and clients
Develop and enhance testing, validation, back-testing, and quality-control frameworks
Contribute to the team’s AI transformation journey, with a focus on applying AI, ML, and automation to model governance processes, such as model validation and back-testing, testing and quality control, documentation and reproducibility checks, and research-to-production code migration
Clearly document and communicate model assumptions, results, and limitations to both technical and non-technical audiences