Position: Quantitative Analyst - Portfolio Management | Credit/ Fixed Income
Location: New York
Our client is a leading global alternative asset manager with over $100 billion in assets under management. The firm operates at the intersection of sophisticated quantitative research and active portfolio management, deploying capital across a broad range of strategies with a strong emphasis on analytical rigor and data-driven decision-making.
The firm is seeking a talented Quantitative Analyst to join its Portfolio Management team, embedded directly within the investment process across public credit and fixed income strategies. This is a high-impact role for a quantitatively minded professional who thrives at the intersection of financial theory, data engineering, and portfolio construction and who wants their work to directly influence live investment decisions at scale.
The Quantitative Analyst will sit within the Portfolio Management team, working closely with portfolio managers, researchers, and risk professionals to develop and maintain the analytical infrastructure that drives investment decisions across the firm's public credit and fixed income portfolios. The role spans the full lifecycle of quantitative research from data ingestion and model development through to portfolio construction, optimization, and the delivery of actionable investment recommendations.
Key Responsibilities
- Support and enhance portfolio construction and asset allocation processes across public credit and fixed income strategies, with a focus on improving risk‑adjusted returns and capital efficiency.
- Develop, implement, and maintain quantitative models for portfolio optimization, factor analysis, scenario analysis, and stress testing.
- Build and own analytics tools, dashboards, and reporting infrastructure that provide portfolio managers with real‑time insight into positioning, risk exposures, and performance attribution.
- Conduct original market research across public credit and fixed income markets, identifying themes, anomalies, and opportunities through rigorous data analysis.
- Generate portfolio recommendations supported by quantitative analysis, clearly communicating findings and model outputs to portfolio managers and investment teams.
- Collaborate with technology, data, and risk teams to ensure data integrity, scalability of tooling, and alignment with firm‑wide infrastructure.
- Monitor and evaluate model performance on an ongoing basis, iterating and improving as market conditions evolve.
Requirements
- An advanced degree (Master's or PhD) is highly preferred. Ideally in mathematics, statistics, computer science, engineering, physics, or a related field.
- 2–7 years of relevant experience in a quantitative research, quantitative analytics, or quantitative portfolio management role, ideally within a hedge fund, asset manager, investment bank, or comparable environment.
- Deep expertise in Python, including experience building production‑grade analytical tools, data pipelines, and modelling frameworks. Proficiency in relevant libraries such as Num Py, Pandas, Sci Py, and scikit‑learn is expected.
- Strong SQL skills, with experience querying and managing large financial datasets across relational databases.
- Solid understanding of fixed income instruments and markets, including investment grade and high yield corporate credit, rates, structured products, and related derivatives.
- Hands‑on experience with portfolio construction and optimization techniques, including mean‑variance optimization, factor modelling, and risk budgeting.
- Familiarity with risk frameworks and performance attribution methodologies relevant to fixed income and credit portfolios.
- Experience building data visualization tools and dashboards. Proficiency with tools such as Plotly, Dash, Tableau, or similar platforms is a plus.
- Exceptional analytical and problem‑solving skills, with the ability to translate complex quantitative findings into clear, actionable investment insights.
- Strong communication skills and the ability to collaborate effectively with both technical and non‑technical stakeholders across the investment team.
- A proactive, intellectually curious mindset with a genuine interest in markets and a drive to continuously improve models and processes.
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