The Investment Risk team at Numeric needs to scale its approach to identify and prioritize risks in investment portfolios due to exploding data volumes, increased customization of client portfolios, and growing complexity in maximizing expected return and minimizing expected risk.
Requirements
- Can write clean, modular Python and are comfortable with pandas, NumPy, file I/O, and automation
- Know how to use git and navigate a Linux/bash environment with confidence
- Love building systems that scale your own thinking
- Excellent grasp of matrix algebra.
- Willingness to use cutting-edge AI tools.
Responsibilities
- Maintain and evolve our Python-based analytics pipeline used daily to assess portfolio risk, factor exposures, and signal behavior
- Support stakeholder needs with thoughtful delivery of scheduled reports, board materials, attestations, and ad hoc analyses—with a strong push to automate wherever possible
- Leverage our world-class GenAI platform and centralized compute resources to accelerate throughput and reduce manual workflows
- Collaborate with central technology, investment teams, and various risk stakeholders—building relationships while staying focused on delivery of whatever gets the job done
- Build tools that affords faster insight generation, including: Portfolio evaluation dashboards
- Risk decomposition utilities
- Interactive visualization platforms
Other
- Thinking critically about the investment process in order to provide an independent perspective about what risks are being taken in Numeric portfolios - and communicating insights that may potentially result in enhancements to the investment process in the form of sharper systematic decision making.
- Frequent interaction with the Investment Committee, regular meetings with the various investment teams, and ongoing discourse with Portfolio Managers and Researchers.
- Ongoing care and feeding of the Ultraverse - an internal platform for proprietary risk analytics, designed for the purpose of statistically analyzing the Numeric investment process and the reward to risk trade-offs being made in both signals and portfolios.
- Have a background in Computer Science, Applied Math, Engineering, Physics, or similar
- Enjoy delivering real value quickly—and improving processes over time
- Can balance delivery of recurring tasks with ongoing project work
- Possess an intrinsic drive and an appetite for learning
