Credit Portfolio Lending Group – Quantitative Research – Associate or Vice President

Overview

In this role, you will design and deploy AI-enabled tools for trading workflows and credit portfolio management within a global QR function. You’ll advance automation and digital transformation, enhancing trading signals, hedging strategies, and risk reporting. You work with cross-functional teams to apply AI, LLMs, and agent-based systems to real-time markets and portfolio optimization. This is a chance to influence credit risk decisions at scale in a leading financial institution.

Responsibilities

• Design, build, and deploy AI/LLM-driven tools for trading workflows
• Apply machine learning to improve trading-signal generation and portfolio optimization
• Identify practical AI applications for daily desk operations and automation
• Use credit portfolio data to uncover opportunities and optimal hedging strategies
• Collaborate with model control teams for model review and compliance
• Automate daily risk and position reports to support broader automation
• Develop trade support and automation infrastructure for hedging and loan sales
• Engage with other Markets QR sub-teams to share best practices

Key requirements

• Advanced degree in mathematics, statistics, physics, financial engineering, computer science, or equivalent
• Experience in quantitative research or analytics supporting fixed income, credit, or lending desks
• Hands-on experience building and deploying AI agents, ML, or LLM-based applications for financial markets or trading workflows
• Experience in portfolio optimization and related software/tools
• Strong Python programming and data science skills
• Excellent analytical, numerical, and problem-solving abilities with interest in credit markets
• Strong written and verbal communication, capable of explaining complex concepts to diverse stakeholders
• Ability to thrive in a high-pressure, collaborative team environment

• collaboration across global teams
• communication of complex ideas to technical and non-technical audiences
• intellectual curiosity and learning mindset
• Python
• AI/LLM
• machine learning

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