Associate

About the Role

The Quantitative Trading & Research team develops quantitative models, algorithms, analytical capabilities, and agentic workflows to enhance client engagement for Cash Equities Sales teams. By transforming complex client flow and transaction data into actionable signals and insights, the team supports client workflows.

As an Associate in the Quantitative Trading & Research team, you will:

• Develop quantitative models, algorithms, analytical capabilities, and agentic workflows to improve client engagement for Cash Equities Sales teams.
• Transform complex data, including client flow and transactional data (trades, orders, axes, IOIs), into relevant signals, client targeting insights, "who to call" recommendations, LLM-generated dynamic client content, trade ideas, and post-trade opportunities.
• Own the research lifecycle from problem framing and data preparation through methodology, evaluation, production implementation, and monitoring, in partnership with Technology, Data, and Product teams.
• Utilize statistical methods, ML, LLMs, and AI agents where they add value.

Key Responsibilities

• Research, develop, and validate models and algorithms for data processing, signal identification, client targeting, opportunity prioritization, and Sales analytics.
• Construct reproducible research datasets and develop methods for extracting, enriching, and interpreting structured and unstructured data, including Cash Equities flow and transactional data (executions, orders, axes, IOIs, blocks).
• Analyze client trading behavior to identify target clients, relevant ideas, and optimal timing.
• Develop "who to call" recommendation models that rank clients for Sales coverage by combining flow signals, axes, liquidity, trade ideas, and market events, with clear rationale.
• Build reusable executable analytical capabilities, including tools, skills, and plugins, to encode research methods for use in Sales applications and AI-assisted workflows.
• Design, build, and evaluate agentic workflows that orchestrate tools, data services, and models to automate multi-step Sales tasks, with appropriate guardrails, human-in-the-loop review, and auditability.
• Develop dynamic, data-derived client content grounded in analytical insights, with clear source attribution and supporting rationale, for outreach and responses to inbound requests.
• Use LLMs to generate personalized client content (e.g., talking points, flow commentary, trade idea summaries, outreach drafts), grounded in underlying data through retrieval, with controls for factual accuracy, hallucination, tone, and compliance.
• Develop algorithms to generate trade ideas and identify post-trade opportunities using client positions, historical flow, objectives, product characteristics, and market conditions, and match ideas, axes, and liquidity to relevant clients.
• Design experiments and evaluation benchmarks, compare approaches against baselines, and use error analysis and Sales feedback to improve analytical quality and business impact.
• Implement and maintain production-quality analytical code, with testing, reproducibility, explainability, and monitoring of model, agent, and output performance.

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 large-scale transactional or time-series data, 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 Cash Equities, including client flow, execution data, axes/IOIs, block trading, or market microstructure.
• Experience with client portfolios or positions, Sales analytics, or trade-lifecycle opportunities.
• Experience analyzing financial or time-series datasets to identify actionable signals.
• Experience developing "who to call", client targeting, 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 retrieval-augmented generation (RAG), prompt design, evaluation, and monitoring.
• Experience designing and evaluating agentic workflows (e.g., tool-calling agents, multi-step orchestration, planning and retrieval), including guardrails, evaluation harnesses, and observability.
• Experience integrating analytical code into applications or AI agents through reusable tools, skills, plugins, or MCP interfaces.