Portfolio Risk Modeling - Associate

About this role

BlackRock is one of the world's preeminent asset management firms and a premier provider of global investment management, risk management and advisory services to institutional, intermediary, and individual investors around the world. BlackRock offers a range of solutions - from rigorous fundamental and quantitative active management approaches aimed at maximizing outperformance to highly efficient indexing strategies designed to gain broad exposure to the world's capital markets. Our clients can access our investment solutions through a variety of product structures, including individual and institutional separate accounts, mutual funds and other pooled investment vehicles, and the industry-leading iShares® ETFs.

Aladdin Financial Engineering (AFE)

AFE is a diverse and global team with a keen interest and expertise in all things related to technology and financial analytics. The group is responsible for the research, development, and adoption of quantitative financial and behavioral models and tools across many different areas - single-security pricing, prepayment models, risk, return attribution, liquidity, optimization and portfolio construction, scenario analysis and simulations, covering all asset classes. The group is also responsible for the technology platform that delivers those models to our internal partners and external clients, and their integration with Aladdin.

We are seeking a hands-on Quantitative Associate to join the Portfolio Risk team within AFE. This role is ideal for someone who enjoys working deeply with data and code, has strong attention to detail, and is motivated to build practical, production-ready risk models and analytics used by real investment professionals.

This is an individual contributor role focused on quantitative research, model development, testing, and implementation. Formal project management responsibilities are not required, but the role does require strong ownership of work, critical thinking, and the ability to collaborate effectively with researchers, engineers, and stakeholders across regions.

• Multi-factor Linear risk models

• Value-at-Risk (VaR) methodologies

• Volatility and covariance matrix estimation

• Portfolio stress testing and scenario analysis

• 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

• Research-to-production code migration

• Clearly document and communicate model assumptions, results, and limitations to both technical and non-technical audiences

• 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

• 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