Quantitative Trading & Research - Credit & Public Finance Sales - Associate

Job Description The JPMorgan Quantitative Trading & Research (QTR) Sales team builds products which directly support the 2000+ member Sales organization. In doing so, we leverage JP Morgan’s vast portfolio of data and analytics and partner closely with Sales, Trading, Trading QTR, Digital Markets, Technology, and other groups. Job Summary: As an Associate for the Quantitative Trading & Research team, you will primarily focused on supporting Credit & Public Finance Sales. This global sales team distributes corporate & municipal bonds, loans, CLOs, CDS, credit indices, and other corporate-credit related products. You will turn complex data into client-relevant signals, content and trade ideas that support both inbound and outbound client workflows, and own each project end to end: from problem framing and data preparation through methodology, evaluation, production implementation and monitoring. Most but not all coding will be in Athena, a python-based JPM framework. Job Responsibilities: Generate client-tailored marketing materials and analytical content for JPM inventory, with clear source attribution and supporting rationale, sent directly to sales and/or clients, to increase client satisfaction, draw more clients onto our proprietary platforms, and increase client “stickiness” Develop relative-value and analytics tools that match client interests and holdings with available inventory, generating trade ideas and prioritizing the most actionable opportunities Build tools and data pipelines that automate or aid sales workflows, combining structured and unstructured market, trading and client data from multiple sources with automated data-quality checks Design, prototype, build and support AI agents that support sales workflows, for example LLM-based extraction of structured information from unstructured text, with rigorous evaluation against benchmarks and baselines, using error analysis and Sales feedback to improve accuracy Package analytical methods as reusable tools, skills, plugins and MCP servers that Sales applications and AI agents can call Measure the impact of tools and content (usage, client engagement, idea performance) and use the results to set priorities in partnership with Digital Markets Partner with Sales to identify the most valuable problems, and with Technology and Digital Markets to integrate our tools into shared platforms, reusing existing data services and contributing back to the shared ecosystem of QTR Sales frameworks Required Qualifications, Capabilities, and Skills: Strong academic background in a quantitative field (e.g., Mathematics, Physics, Statistics, Economics, Computer Science, Data Science, Engineering, Quantitative Finance). Masters-level or equivalent experience. Advanced Python programming skills, including code architecture, plus strong SQL and pandas skills. Ability to manipulate and analyze complex, large-scale, high-dimensionality data from varying sources, with reproducible data preparation, feature engineering and validation. Good software engineering practice: modular, testable and maintainable design, version control, code review and monitoring Strong foundations in probability, statistics and machine learning, with experience designing and evaluating models against real business problems: running rigorous experiments, diagnosing failure modes and demonstrating measurable improvements Strong ownership and ability to work autonomously on multiple projects at once, communicating directly with front-office stakeholders and turning loosely defined requests into clearly scoped deliverables Ability to explain analytical methods, assumptions and trade-offs clearly to both technical and non-technical stakeholders Demonstrated desire to learn and leverage the evolving GenAI revolution Preferred Qualifications, Capabilities, and Skills: Experience in a quantitative role supporting trading or sales, especially Credit-related, including familiarity with fixed-income concepts (yields, spreads, duration) Experience analyzing financial, client-interaction (e.g., RFQs, chats) or time-series datasets to identify actionable signals Hands-on experience building LLM applications (tool-calling, structured outputs, retrieval, evaluation, monitoring), including exposing analytical code to AI agents through tools, skills, plugins or MCP interfaces Experience with recommender, ranking or prioritization systems, entity resolution, or NLP