Quantitative Modeling & AI Director

About this role BlackRock is one of the world’s preeminent asset management firms and is a premier provider of global investment management, risk management and advisory services to institutional, intermediary, and individual investors around the world. BlackRock’s mission is to create a better financial future for our clients. We have a responsibility to be the voice of the investor, and we represent each client fairly and equally. Constant communication with a diverse team of partners strengthens us and delivers better results for our clients. Continuous innovation helps us bring the best of BlackRock to our clients. 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. Key Responsibilities AI & Agentic Architecture Architect and lead development of autonomous agent systems for credit analysis, including self-improving chat agents, event-driven orchestration, and feedback loops for continuous agent improvement Design and implement agentic AI workflows using supervised architectures (e.g., LangChain/LangGraph) that plug into BlackRock’s broader AI assistant platform Drive the technical vision for AI-powered credit intelligence features, balancing innovation velocity with responsible AI principles and production reliability Knowledge Graph & Data Infrastructure Own and evolve the credit knowledge graph (3.5M company nodes, 53M relationships), ensuring data quality, scalability, and clean integration with upstream and downstream systems Lead data lake unification across multiple sources (regulatory filings, market data feeds, proprietary datasets), building reliable ingestion pipelines with high reconciliation accuracy Design APIs and data services to provide clients with novel ways of leveraging credit analytics and knowledge graph insights Quantitative Credit Modeling Train, evaluate, and productionize ML models for credit risk — including graph neural networks for relationship-aware default prediction, Bayesian/MRF frameworks for modeling correlated defaults, and temporal convolutional networks for time-series credit signals Expand private credit analytics coverage to address client needs across direct lending, CLOs (collateralized loan obligations), BDCs (business development companies), and asset-based finance Maintain and enhance financial models following rigorous development lifecycle practices, including backtesting, validation, and ongoing monitoring Engineering Leadership Lead a team of data scientists, modelers, and ML practitioners, fostering a culture of technical excellence, continuous learning, and operational rigor Drive execution across multiple workstreams with cross-functional partners in product management, platform engineering, and data teams Communicate technical concepts clearly to non-technical stakeholders, including investment professionals and senior leadership Attract, retain, and grow engineering talent, mentor team members across experience levels Minimum Requirements A bachelor’s degree in quantitative discipline in mathematics, physics, mechanical engineering, electrical engineering, computer engineering, or similar. Advanced degrees or equivalent professional experience in engineering systems, AI/ML applications, or financial technology are strongly preferred Strong practical knowledge of products, modeling methodologies, and analytics in the rates and derivatives space. Experience with enterprise knowledge graph architecture. Excellent communication skills. Ability to work horizontally across various functions, including portfolio managers a ... (truncated, view full listing at source)