Overview 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. Role We are looking to hire a Senior modeler to join our Portfolio Risk team, to lead model governance with the potential to grow into regional lead of our group in this region. The individual will have a strong background in quantitative research, demonstrable project management skills, and the ability to guide junior modelers and engineers while collaborating with senior modelers from other groups/regions. The focus will be on analytics project work related to streamlining the development of new portfolio risk models, expanding our model testing framework, and building a robust research platform. Responsibilities Lead all aspects of model governance initiatives. Guide junior modelers and engineers. Collaborate with senior modelers from other groups/regions. Streamline development of new portfolio risk models. Expand model testing framework. Build a robust research platform. Represent the models and analytics to stakeholders and Aladdin clients in the Americas region. Qualifications Research/Analytics professional with 7+ years of solid experience in quantitative/statistical modeling. Advanced degree in a quantitative discipline (e.g., Computational Finance, Financial Engineering, Statistics, Applied Mathematics, Econometrics). Experience with market risk/factor models and portfolio risk analytics (VaR, Tracking Error, Stress Testing). Familiarity with financial products and risk management processes. Exposure to model backtesting, quality controls, and validation. Hands‑on experience with statistical software (Python, R, MATLAB). Strong programming background in Python. Experience with large data sets and machine learning algorithms. Prior work experience in financial modeling (risk models, analytics) or data science and model deployment to production environment. #J-18808-Ljbffr