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BlackRock is seeking a hands-on Quantitative Associate to join the Portfolio Risk team within Aladdin Financial Engineering (AFE). This is an individual contributor role focused on quantitative research, model development, testing, and implementation. The team develops and maintains a broad set of analytics, including multi-factor linear risk models, Value-at-Risk (VaR) methodologies, volatility and covariance matrix estimation, and portfolio stress testing and scenario analysis.
Job Responsibility:
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
Clearly document and communicate model assumptions, results, and limitations to both technical and non-technical audiences
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
Nice to have:
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