Principal - Quant Trading

Position Overview

We are seeking a Principal to join Apollo’s PM & Trading Strats team within Global Corporate Credit (GCC), based in London. GCC Strats sits inside the business and serves as the technical and quantitative resource for PM and Trading. In this role, you will work directly with senior traders and business leadership to identify opportunities, develop ideas, and drive execution on tools, analytics, and workflows that improve decision-making, risk management, execution, and investment performance.

This role will be primarily focused on the London trading business, covering Corporate Bonds, CDS, Loans, Equities, and Macro trading. We are looking for someone highly analytical, technically strong, and commercially minded – someone who can engage credibly with senior stakeholders, present ideas clearly, take ownership, and get high-impact work over the line. The candidate requires strong judgment, a bias toward action, and the ability to turn real desk problems into scalable tools and actionable analytics. As artificial intelligence reshapes investment workflows, this person will help identify and drive high impact use cases across trading into production, including bringing research content such as analyst views and issuer intelligence more directly into day-to-day decision making.

About The Team

The mandate of PM & Trading Strats is to contribute to investment performance by building the analytics, tools, and automation used by Traders and PMs across Global Corporate Credit. PM & Trading Strats turns Apollo’s market data, internal portfolio context, research content, and other investment inputs into actionable, real-time outputs that improve pricing and execution decisions, strengthen risk management, and systematically surface the best opportunities across markets. The team also develops portfolio construction and decision tools that help PMs allocate capital, integrate new deal flow, and manage liquidity more deliberately through stronger start of day views, ranked adds/trims/switches, and automated flagging of drift, inefficiencies, and risk issues. The mandate includes laying the foundation for scalable systematic capabilities across the business and connecting research, data, and analytics more directly to day to day investment decision making.

This role reports directly to the COO of the business and also into the firm’s core Quant team, helping align business priorities with consistent quantitative excellence, technical discipline, and scalable execution across the Credit platform.

Primary Responsibilities:

Build and own quantitative tools & analytics that support alpha generation for London Trading, while coordinating closely with peers in NY to ensure alignment across the broader platform.
Drive end-to-end delivery of desk critical tools, working directly with traders and business leadership to identify opportunities, source data, design models or logic, and lead implementation, rollout, and iteration.
Lead alpha research and signal development by structuring datasets, running backtests and analysis, pressure testing assumptions, and translating outputs into scalable tools and monitoring.
Partner with Engineering and Data to improve the reliability and scalability of Strats tooling, including data pipelines, APIs, compute, monitoring, and documentation.
Help build out systematic capabilities across the London trading business, supporting signal to trade workflows and more scalable decision making across Corporate Bonds, CDS, Loans, Equities, and Rates, in alignment with the broader GCC Strats agenda.
Apply quantitative analysis to day-to-day desk questions, including pricing and relative value, liquidity, risk, positioning, and what changed / why diagnostics, while turning recurring needs into reusable tools over time.
Implement AI enabled workflows across trading, working with Strats, COO, Engineering, and the business to move high impact use cases into production, including areas such as research integration, summarization, and workflow automation.

Qualifications & Experience

7–12 years of experience in quantitative finance, strats, trading analytics, systematic research, risk analytics, or a comparable role on the buy side or sell side.
Strong markets judgment and relevant product knowledge across some combination of corporate credit, CDS, loans, equities, and rates.
Strong programming skills, with Python required, and a track record of building real tools used by investment teams, including analytics, dashboards, screens, alerts, and data pipelines. C++ or Java is a plus.
Solid foundation in statistics, applied math, and modeling, with practical experience working with large and varied datasets including market data, issuer level data, positions, exposures, and liquidity measures.
Proven ability to operate in a high touch environment with traders and senior stakeholders, with strong communication skills, clear ownership, and sound prioritization instincts.
Experience identifying high value opportunities, presenting ideas clearly, and driving execution from concept through delivery.
Exposure to AI and ML is a plus, especially LLM enabled workflows such as retrieval, search, extraction, summarization, and evaluation, but the core requirement is delivering scalable tooling that improves investment decision making.
Degree in quantitative discipline such as Mathematics, Statistics, Physics, Engineering, Computer Science, or a similar field. Advanced degree preferred but not required with strong relevant experience.

Details

Location

London, UK

Experience

Min. 7 years

Position Overview

About The Team

Primary Responsibilities:

Build and own quantitative tools & analytics that support alpha generation for London Trading, while coordinating closely with peers in NY to ensure alignment across the broader platform.
Drive end-to-end delivery of desk critical tools, working directly with traders and business leadership to identify opportunities, source data, design models or logic, and lead implementation, rollout, and iteration.
Lead alpha research and signal development by structuring datasets, running backtests and analysis, pressure testing assumptions, and translating outputs into scalable tools and monitoring.
Partner with Engineering and Data to improve the reliability and scalability of Strats tooling, including data pipelines, APIs, compute, monitoring, and documentation.
Help build out systematic capabilities across the London trading business, supporting signal to trade workflows and more scalable decision making across Corporate Bonds, CDS, Loans, Equities, and Rates, in alignment with the broader GCC Strats agenda.
Apply quantitative analysis to day-to-day desk questions, including pricing and relative value, liquidity, risk, positioning, and what changed / why diagnostics, while turning recurring needs into reusable tools over time.
Implement AI enabled workflows across trading, working with Strats, COO, Engineering, and the business to move high impact use cases into production, including areas such as research integration, summarization, and workflow automation.

Qualifications & Experience

7–12 years of experience in quantitative finance, strats, trading analytics, systematic research, risk analytics, or a comparable role on the buy side or sell side.
Strong markets judgment and relevant product knowledge across some combination of corporate credit, CDS, loans, equities, and rates.
Strong programming skills, with Python required, and a track record of building real tools used by investment teams, including analytics, dashboards, screens, alerts, and data pipelines. C++ or Java is a plus.
Solid foundation in statistics, applied math, and modeling, with practical experience working with large and varied datasets including market data, issuer level data, positions, exposures, and liquidity measures.
Proven ability to operate in a high touch environment with traders and senior stakeholders, with strong communication skills, clear ownership, and sound prioritization instincts.
Experience identifying high value opportunities, presenting ideas clearly, and driving execution from concept through delivery.
Exposure to AI and ML is a plus, especially LLM enabled workflows such as retrieval, search, extraction, summarization, and evaluation, but the core requirement is delivering scalable tooling that improves investment decision making.
Degree in quantitative discipline such as Mathematics, Statistics, Physics, Engineering, Computer Science, or a similar field. Advanced degree preferred but not required with strong relevant experience.