Portfolio Risk Quantitative Modeler, Associate - Aladdin Financial Engineering

BlackRock•New York, NY

8d•$137,500 - $170,000•Hybrid

About The Position

BlackRock is seeking a hands-on Quantitative Associate to join the Portfolio Risk team within Aladdin Financial Engineering (AFE). AFE is a global team focused on the research and development of quantitative financial and behavioral models and tools across various asset classes and financial analytics areas. This role involves quantitative research, model development, testing, and implementation, with a focus on building 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. These models are crucial for investment and risk management decisions within Aladdin. The role also offers the opportunity to contribute to the team's AI transformation 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.