Associate

About the Role

The Quantitative Trading & Research team supports the Digital Agenda by building quantitative models and agentic workflows that automate the Access lifecycle and strengthen how Sales and Research teams engage with clients across asset classes. The team applies statistical, machine learning, and large language models (LLMs) to client profiles, interactions, coverage efforts, and Access activity to improve investor and client targeting, "who to call" recommendations, Access monetization, and wallet predictions.

Job Summary

As an Associate on the team, you will develop and operationalize quantitative models and agentic workflows that help automate the Access lifecycle and improve client engagement. You will work across statistical, machine learning, and LLM-based approaches, taking solutions from initial problem definition and data preparation through evaluation, production deployment, and ongoing monitoring. You will also partner with Technology, Data, and Product teams to integrate your work into tools and workflows used by Sales and Research.

Key Responsibilities

• Research, develop, and validate statistical, machine learning, and LLM-based models for client targeting, opportunity prioritization, signal identification, and Sales and Research analytics.
• Build propensity, recommendation, and ranking models that identify investors and clients for Access opportunities, with clear rationale and appropriate baselines.
• Develop investor-to-corporate and investor-to-analyst matching models for Access events using client profiles, interests, holdings, flows, research engagement, participation, and feedback.
• Create wallet and monetization models to estimate client wallet and share-of-wallet, and assess the impact of Access activity, interactions, and coverage on revenue and client value.
• Model client engagement and Sales and Research coverage using interaction, meeting, event, and research-consumption data, including time-to-event and attrition signals; build reusable cross-asset client profiles and embeddings.
• Design agentic workflows to coordinate multi-step Sales, Research, and Access tasks, including demand capture, event invitations, scheduling, meeting preparation, summaries, follow-ups, and inbound requests.
• Build workflows that use targeting and matching models to support Access origination and scaling, personalize outreach, coordinate scheduling, capture feedback, and update client profiles, with human review where appropriate.
• Develop agentic coverage workflows that monitor client activity and surface "who to call" recommendations with relevant context and suggested next steps.
• Create reusable analytical tools and agent capabilities, and design architectures with appropriate planning, retrieval, tool selection, guardrails, human oversight, and auditability.
• Use LLMs to generate personalized client content grounded in source data, with controls for attribution, accuracy, tone, and compliance; evaluate models and workflows through benchmarks, backtests, experiments, and user feedback.
• Build reproducible datasets and feature pipelines, maintain production-quality code and monitoring, and partner with Sales, Research, Access, Technology, Data, and Product to integrate solutions and measure their impact.

Requirements & Tech Stack

• Strong Python and SQL skills, with experience developing modular, testable analytical software for production use.
• Strong quantitative foundations in probability, statistics, numerical methods, machine learning, or related disciplines.
• Experience designing, implementing, and evaluating models, algorithms, or analytical techniques against real business problems.
• Hands-on experience working with complex datasets, including data preparation, feature engineering, validation, and reproducible analysis.
• Ability to design rigorous experiments, interpret results, diagnose failure modes, and demonstrate measurable improvements.
• Ability to explain analytical methods, assumptions, and trade-offs clearly to technical and non-technical stakeholders.
• Practical judgment and ownership of research delivery, with appropriate handling of confidential data and adherence to established risk and change controls.

Qualifications

• Advanced degree in Mathematics, Statistics, Computer Science, Data Science, Physics, Engineering, Quantitative Finance, or a related field.

Preferred Qualifications

• Experience with derivatives products, client portfolios or positions, Sales analytics, or trade-lifecycle opportunities such as rolls.
• Experience analyzing financial, client-interaction, or time-series datasets to identify actionable signals.
• Experience developing recommendation or prioritization algorithms, or dynamic analytical client content with traceable sources.
• Experience applying ML or LLMs to data interpretation, retrieval, content generation, or decision support, including evaluation and monitoring.
• Experience integrating analytical code into applications or AI agents through reusable tools, skills, plugins, or MCP interfaces.