Join AllianceBernstein's Systematic Fixed Income team to develop, backtest, and implement systematic fixed-income and credit strategies. Responsibilities include portfolio optimization, factor discovery and analysis, risk attribution, and contributing to a Python-based research platform (abAlphaLabs). Collaborate across the investment process and support ongoing management and enhancement of systematic strategies.
Who You’ll Work With
AllianceBernstein’s Systematic Fixed Income team develops and manages cutting-edge, high-performance, fully systematic, factor-driven fixed-income portfolios.
What You’ll Do
We are seeking a quantitative researcher focused on systematic fixed-income and credit strategies to join our London office. The successful candidate will collaborate with colleagues across the investment process and contribute to the development, implementation, and management of systematic strategies within AllianceBernstein’s Fixed Income division.
Responsibilities include, but are not limited to:
- Developing and evaluating systematic investment strategies through simulations, backtesting, and strategy analysis.
- Working on portfolio optimization, data science, and quantitative research problems.
- Conducting factor discovery, factor return analysis, and risk attribution.
- Contributing to our quantitative research environment, abAlphaLabs, a Python-based research platform.
- Taking a hands-on role in the management and ongoing enhancement of systematic fixed-income strategies.
What We’re Looking For
The ideal candidate will have:
- An advanced degree in Finance, Financial Engineering, Mathematics, Computer Science, Operations Research, Economics, Electrical Engineering, or a related field.
- Strong Python programming skills and deep familiarity with the Python ecosystem.
- Experience working with SQL databases.
- Excellent attention to detail, a strong focus on quality, and the ability to take ownership of projects.
- Knowledge of fixed-income securities and markets, which is preferred but not required.
- Deep desire to understand and outperform the markets.
- Data science and machine learning skills are a strong plus.
- Exposure to modern development and operations tools, such as Airflow, Kubernetes, or similar technologies, is a plus.
Prior fixed-income trading or portfolio management experience is not required.
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