Quantitative Researcher - Credit Algo Trading (VP)
- Design and implement algorithmic pricing models for credit trading using numerical techniques such as Monte Carlo simulations and PDE solvers.
- Build and maintain analytics libraries for pricing, risk management, and automated market making.
- Analyze financial time series and market data to extract actionable insights for trading strategies.
- Collaborate closely with Traders, Structurers, Technologists, and Control Functions (Legal, Compliance, Risk, Audit).
- Ensure robust governance and risk management practices in all model development and deployment.
- Utilize Python and kdb+/q extensively for research, prototyping, and production implementation of trading algorithms.
- PhD strongly preferred in Mathematics, Physics, Computer Science, or a related STEM/quantitative field.
- Deep expertise in both kdb+/q and Python - this is a non-negotiable requirement.
- 4-10 years of experience in quantitative modeling or analytics, ideally within financial markets.
- Strong programming skills in Python, kdb+/q, and familiarity with C++, C#, .NET, SQL.
- Solid understanding of statistics, probability theory, and numerical methods.
- Experience with credit algo trading or electronic trading analytics is preferred.
- Candidates from equities e-trading or central risk book (CRB) backgrounds will also be considered if they meet the PhD and technical requirements.
- Work on auto-quoting and RFQ systems for corporate bond trading.
- Help shape the future of automated market making in credit markets.
- Exposure to a wide range of technologies and quantitative techniques.
- High visibility and collaboration across trading, risk, and technology teams.
- Strong emphasis on responsible finance, governance, and ethical decision-making.
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