AI Labs has offices in New York, Palo Alto, and Edinburgh. The team has several Stanford professors as senior advisors with world-class expertise in machine learning, statistics, optimization and stochastic control. These advisors include Emanuel Candes, Trevor Hastie, Robert Tibshirani, and Mykel Kochenderfer who dedicate time in our Palo Alto office and provide advice and mentorship for all members of the distributed team.
- Work closely with data engineering and infrastructure to build out end to end solutions.
- Build new optimization based products, and evaluate and improve BlackRock’s products.
- You will work with a multi-discipline, multi-region team of outstanding data scientists, engineers, and investment professionals on a corporate-wide set of client, investor, and operational problems. This can include problems in a multitude of areas including but not limited to: portfolio construction, retirement planning, tax-aware transitions, and bond index tracking.
- Work on complex problems in optimization. Frame the problem so the optimization is tractable while still meeting the core application needs.
- In conjunction with the team, conduct end to end analysis that consists of information gathering, requirements specification, processing, analysis, algorithms, builds, ongoing results, and presentations for specific projects. This will include but not be limited to fitting optimization models such as Markowitz portfolio construction, index tracking, or Merton style financial planning problems.
- Background in basic mathematical and optimization principles such as linear algebra, first and second order optimization algorithms, and convex optimization.
- Familiarity with Python.
Qualification & Experience:
- MS degree or PhD in a quantitative field (computer science, mathematics, statistics, economics, physics, engineering or related field).
- Experience applying optimization to real world problems, such as in robotics, portfolio construction, or machine learning.
Vacancy Type: Full Time
Job Location: San Jose, CA, US
Application Deadline: N/A
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