Financial Quantitative Modeler, Aladdin Financial Engineering, Associate

About this role About Aladdin Financial Engineering (AFE): Join a diverse and collaborative team of over 400 modelers and technologists in Aladdin Financial Engineering (AFE) within BlackRock Solutions, the business responsible for the research and development of Aladdin’s financial models. This group is also accountable for analytics production, enhancing the infrastructure platform, and delivering analytics content to portfolio and risk management professionals (both within BlackRock and across the Aladdin client community). The models developed and supported by AFE span a wide array of financial products covering equities, fixed income, commodities, derivatives, and private markets. AFE provides investment insights that range from an analysis of cash flows on a single bond, to the overall financial risk associated with an entire portfolio, balance sheet, or enterprise. The Private Markets SWAT Team: This team provides quantitative modeling solutions for private markets across a wide variety of problems, including nowcasting private asset and fund values, forecasting alpha in private asset deals, and optimizing strategic asset allocation with illiquid assets. The team builds and connects innovative models and methodologies to support investment decision-making in illiquid asset classes. Role Description: We are seeking an associate-level quantitative researcher to support and expand our growing suite of models for estimating the value of private assets and producing next-generation, real-time private market indices. The role centers on the development, operation, and continuous enhancement of Nowcasting models—managing day-to-day index estimation, improving model performance, and ensuring a robust, high-quality production process. A key challenge will be adapting these models to new situations, data conditions, and asset-class features, and determining when extensions or methodological changes are appropriate. The position sits within the Private Markets SWAT Team, a small group focused on solving novel quantitative problems in private markets, and will provide exposure to a broad range of empirical, modeling, and applied research projects across the organization. Key Responsibilities: Manage and enhance the team’s existing Nowcasting model for private assets, including calibration, monitoring, diagnostics, and performance evaluation. Contribute to the research, design, and development of new Nowcasting and valuation models across additional private asset classes. Conduct empirical research to calibrate models to private market data and assess model performance through backtesting, benchmarking, and robustness analysis. Implement, maintain, and productionize model codebases, ensuring reliability, transparency, and reproducibility. Partner with internal stakeholders to understand use cases, communicate model behavior and limitations, and support applications of the models in investment and analytical workflows. Participate in broader Private Markets SWAT Team projects, including bespoke modeling work for portfolio management teams and quantitative analyses for financial advisory or product-specific initiatives. Qualifications: Master’s degree in Financial Engineering, Finance, Statistics/Econometrics, Mathematics, Computer Science, or a related quantitative field; PhD preferred. 3 years of experience in financial modeling role or equivalent. Strong grounding in empirical modeling techniques, especially time-series analysis and Bayesian methods. Demonstrated ability to conduct rigorous empirical research, including handling complex datasets and designing well-structured analyses. Effective communicator capable of explaining quantitative concepts clearly and collaborating across technical and non-technical teams. Proficient programming skills in Python with the ability—and desire—to adopt new tools, libraries, and technologies as needed. Preferred: Experience with private assets (e.g., venture capital, grow ... (truncated, view full listing at source)