Execution Quant Researcher - Credit

Selby Jennings; New York (NY); Sur place; USD 200 000 - 250 000; Plein temps

Résumé du poste

Selby Jennings is seeking a VP-level Execution Quant Researcher for Credit in New York to lead research on liquidity, market impact, and transaction cost analysis.

You will develop models using TRACE and market data, partner with traders and PMs, and advance production deployment of research into trading desks.

Ideal candidates have strong quantitative skills, experience in fixed income, and familiarity with Python or kdb+/Databricks.

Qualifications

• Experience in quantitative research, trading, portfolio management, or market structure analysis in credit or fixed income.
• Strong statistical and analytical skills; ability to solve complex problems.
• Knowledge of machine learning and AI applications in execution research.
• Excellent communication skills and ability to influence traders and stakeholders.

Responsabilités

• Lead the credit execution research agenda on liquidity and market impact.
• Develop quantitative models using trade, quote, and market data.
• Partner with credit traders and PMs to translate challenges into research.
• Enhance execution analytics platforms with new models and data.
• Monitor credit market structure and liquidity trends.
• Collaborate with researchers, developers, and trading teams to deploy research.

Connaissances

Quantitative research

Trading experience

Portfolio management

Market structure analysis

Statistical analysis

Machine learning

Outils

Python

kdb+/q

Databricks

Description du poste

Execution Quant Researcher - Credit (PR/614885) New York, New York

Salary: USD200000 - USD250000 per year + total compensation of $350K-$400K

Execution Quant Researcher - Credit

Location:

New York, NY

Overview

A leading investment firm is seeking a VP-level Quantitative Research Analyst to lead credit execution research and partner closely with traders and portfolio managers to improve execution outcomes across fixed income portfolios. This role combines quantitative research, market microstructure analysis, liquidity modeling, and transaction cost analysis to drive more efficient trading and decision-making.

Responsibilities

• Lead the credit execution research agenda, focusing on liquidity, market impact, counterparty selection, portfolio trading, execution protocols, and TCA.
• Develop quantitative models using trade, quote, and market data to improve execution performance.
• Serve as the primary research partner to credit traders and portfolio managers, translating business challenges into actionable research.
• Enhance execution analytics platforms through new models, datasets, and performance metrics.
• Monitor developments across credit market structure, electronic trading, and liquidity trends.
• Collaborate with researchers, developers, and trading teams to deploy research into production and drive adoption.

Requirements

• Experience in quantitative research, trading, portfolio management, or market structure analysis within credit or fixed income markets.
• Strong understanding of credit market structure, including RFQ trading, portfolio trading, dealer behavior, ETF-driven liquidity, and transaction cost analysis.
• Experience working with fixed income datasets such as TRACE, quote data, evaluated pricing, and dealer inventory information.
• Strong statistical and analytical skills with the ability to solve complex, ambiguous problems.
• Knowledge of machine learning and AI applications within execution research.
• Excellent communication skills and ability to influence traders, portfolio managers, and senior stakeholders.

Preferred

• Experience with rates or FX markets.
• Familiarity with Python, kdb+/q, Databricks, or similar data platforms.
• Experience working with dealers, trading venues, and market data vendors.

Ideal Background:

A credit market structure expert, execution researcher, quantitative analyst, trader, or portfolio manager with a track record of using data-driven research to improve trading outcomes and execution efficiency.