The Portfolio Simulation Research team is building out a new engine for the joint simulation of the global macro economy, drivers of financial markets, individual assets, and private cashflow. The team is building and connecting innovative frameworks and approaches across these spaces in a Bayesian framework.

Responsibilities for this team include:

• Doing theoretical research to come up with new, or find existing models and methodologies in the risk space, across multiple asset classes including private assets.

• Doing empirical research to calibrate new models to financial data.

• Backtesting, documenting, and guiding new models and methodologies through validation.

• Connect with internal and external clients to identify industry-wide quantitative problems and collaborate with academics affiliated with BlackRock to explore solutions.

• Collaborate on papers for publication, presenting original research at industry conferences, and speaking with institutional clients about relevant research.

Additional job responsibilities may include working with portfolio management teams on custom projects supporting their investment processes or working with financial advisory teams on modeling projects for specific products.

The ideal candidate will have a PhD or equivalent experience in Mathematics, Statistics, Econometrics, Finance, Science, Economics, or other relevant quantitative fields, with 5 to 10 years of experience in quantitative modeling and analytics. They should have a proven track record of conducting high-quality empirical research or theoretical research relevant for empirical analysis, knowledge of financial mathematics (derivatives pricing), and experience with Bayesian or machine learning.

The successful candidate will be able to communicate quantitative information and collaborate optimally in a team environment, and have solid programming skills in Python and a drive and ability to quickly pick up new technologies.

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In this role, you'll turn hypotheses into measurable experiments, advise on test design to ensure validity and statistical significance, and help partners translate learnings into next steps. You'll partner closely with Product, Engineering, and Analytics teams to build a culture of test-and-learn, establish governance standards, and ensure we stay within platform usage limits.

This role turns experimentation into personalization, making every web experience more responsive to who the fan is and what they need.

Responsibilities:

• Build the experimentation program for EA Help,designing, setting up, and managing A/B and multivariate tests in Optimizely.
• Define and maintain experimentation governance, including test intake, prioritization, and documentation processes to ensure consistent, scalable execution.
• Advise on experiment design to ensure tests are statistically valid and align with business goals.
• Set up and launch experiments directly in Optimizely, or partner with Engineering for server-side or complex technical implementations.
• Analyze and interpret experiment results, producing actionable insights, business narratives, and recommendations for next steps.
• Develop personalization strategies using segmentation, targeting, and behavioral data to tailor content to specific fan groups or individuals.
• Integrate qualitative research and fan feedback to validate hypotheses and enrich quantitative findings.
• Pilot new workflows, tools, or automations that improve experimentation velocity, reliability, and transparency.
• Foster a “learn fast” mindset, normalizing failed tests as valuable learning and celebrating insights as much as wins.
• Collaborate cross-functionally with Product, Analytics, and Content teams to identify and prioritize opportunities for testing and personalization.
• Track and report on the performance, adoption, and impact of experimentation across initiatives.
• Monitor platform usage to ensure we stay within monthly active user (MAU) limits and avoid overage charges.
• Stay informed on AI-driven user experiences and explore opportunities to leverage AI for experimentation, personalization, and insights generation.
• Champion experimentation culture, coaching partners and team members to use data to inform design and content decisions.

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