Quantitative Trading & Research - Cash Equities - Associate

The Quantitative Trading & Research team develops quantitative models, algorithms, analytical capabilities and agentic workflows to help Cash Equities Sales teams engage more effectively with clients. By transforming complex client flow and transaction data, including trades, orders, axes and IOIs, into actionable signals, client-targeting insights, “who to call” recommendations, dynamic client content, trade ideas and post-trade opportunities, the team supports both inbound and outbound client workflows Job Summary As an Associate in the Quantitative Trading & Research team, you will develop quantitative models, algorithms, analytical capabilities and agentic workflows that improve how Cash Equities Sales teams engage with clients. You will transform complex data, including client flow and transactional data such as trades, orders, axes and IOIs, into relevant signals, client targeting and "who to call" recommendations, LLM-generated dynamic client content, trade ideas and post-trade opportunities, supporting inbound and outbound client workflows. You will own the research lifecycle from problem framing and data preparation through methodology, evaluation, production implementation and monitoring, partnering with Technology, Data and Product on integration. You will use statistical methods, ML, LLMs and AI agents where they add value. Job 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, including flow patterns, sector and stock preferences, position changes, liquidity needs and trading style, to identify which clients to target, with which ideas, and when. Develop "who to call" recommendation models that rank clients for Sales coverage by combining flow signals, axes and liquidity, trade ideas and market events, with a clear rationale for each recommendation. Build reusable executable analytical capabilities, including tools, skills and plugins, that encode research methods for use in Sales applications and AI-assisted workflows. Design, build and evaluate agentic workflows that orchestrate these tools, data services and models to automate multi-step Sales tasks, such as client pre-call briefings, flow summaries, idea-to-client matching and inbound request handling, 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, such as talking points, flow commentary, trade idea summaries and 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 the most 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, including the reliability, accuracy and usefulness of agent and LLM outputs, recommendation uptake and content quality. Implement and maintain production-quality analytical code, with testing, reproducibility, explainability and monitoring of model, agent and output performance. Required Qualifications 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. Advanced degree in Mathematics, Statistics, Computer Science, Data Science, Physics, Engineering, Quantitative Finance or a related field. 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. 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.