À propos de ce poste Quantitative Researcher - Portfolio Management chez Arootah
Arootah · Hybride · Boston, MA
About the Role
Our client, a leading quantitative investment firm, is seeking a Systematic Fixed Income Portfolio Analyst to support its fixed income and macro investment initiatives. This role offers the opportunity to work alongside experienced portfolio managers, quantitative researchers, and technology professionals in a highly analytical, fast-paced environment.
The ideal candidate is intellectually curious, technically skilled, and passionate about applying data-driven analysis to financial markets. This individual will contribute to portfolio analytics, investment research, risk evaluation, and the ongoing enhancement of systematic investment processes.
Key Responsibilities
- Support portfolio managers and investment teams through quantitative analysis, portfolio monitoring, and market research.
- Develop and maintain analytical tools used for portfolio evaluation and risk assessment.
- Analyze large datasets to identify trends, anomalies, and actionable investment insights.
- Assist in the implementation and refinement of systematic investment approaches across fixed income markets.
- Collaborate with technology and research teams to improve workflow automation and reporting capabilities.
- Evaluate portfolio exposures and assist with performance attribution and risk analysis.
- Produce reporting materials and investment analytics for internal stakeholders.
- Contribute to ongoing research projects related to financial markets, portfolio construction, and investment strategy development.
Qualifications
- Bachelor’s or Master’s degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Finance, Physics, or another quantitative discipline.
- 1–4 years of experience in portfolio analytics, quantitative research, investment management, risk analysis, or financial technology.
- Familiarity with fixed income products, macroeconomic analysis, or systematic investment strategies is a plus.
- Exposure to machine learning, cloud technologies, or data engineering tools is beneficial.
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