Description
The Modeling and Research group at BlackRock is seeking a Quantitative Modeler, Vice President to join their team. The successful candidate will be responsible for developing methodologies, models, and analytics to help portfolio and risk managers better conduct valuation or handle risks and rewards at both security and portfolio level.
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.