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Quantitative Trading & Research - Cash Equity Sales - Vice President at JPMorgan Chase, New York, NY

Quantitative Trading & Research - Cash Equity Sales - Vice President at JPMorgan Chase in New York, NY. Est. up to $130k. Apply with swipeandhired.

The Quantitative Trading & Research team developments quantitative models, algorithms, analytical capabilities and agentic workflows that improve how Cash Equities Sales teams engage with clients
Job Summary
As a Vice President in the Quantitative Trading & Research team, you will lead the development of quantitative models, algorithms, analytical capabilities and agentic workflows that improve how Cash Equities Sales teams engage with clients. You will set the research agenda and lead the transformation of 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 be accountable for the research lifecycle from problem framing and data preparation through methodology, evaluation, production implementation and monitoring, partnering with senior stakeholders across Sales, Trading, Technology, Data and Product. You will use statistical methods, ML, LLMs and AI agents where they add value, and mentor junior team members.
Job Responsibilities

Define and lead a research roadmap for Cash Equities Sales analytics, prioritizing problems by business impact in partnership with Sales and Trading leadership.
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.
Build "who to call" recommendation engines 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.
Architect reusable executable analytical capabilities, including tools, skills and plugins, that encode research methods for use in Sales applications and AI-assisted workflows, and set standards for their design, testing and reuse.
Lead the design, build and evaluation of 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.
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.
Define evaluation frameworks and success metrics, design experiments and benchmarks, compare approaches against baselines, and use error analysis and Sales feedback to drive measurable improvements in analytical quality, business impact and the reliability, accuracy and usefulness of agent outputs.
Own production-quality analytical code and set engineering standards for testing, reproducibility, explainability and monitoring of model, agent and output performance.
Partner with Sales and Trading to identify valuable research problems, and with Technology, Data and Product to integrate analytical capabilities and agentic workflows into shared platforms, retaining ownership of methodology and analytical logic while reusing existing data services and workflow infrastructure.
Mentor and review the work of junior quants, promoting rigorous research practice and code quality.
Ensure appropriate model governance, data controls and adherence to risk, compliance and change management requirements, including for AI and LLM-based components.

Required Qualifications

5+ years of relevant experience in quantitative research, data science or analytics, ideally within Equities at a bank, broker or buy-side firm.
Expert Python and SQL skills, with a proven track record of developing modular, testable analytical software deployed and maintained in production.
Strong quantitative foundations in probability, statistics, numerical methods, machine learning or related disciplines.
Demonstrated delivery of models, algorithms or analytical techniques that solved real business problems with measurable commercial impact.
Experience with Cash Equities, including client flow, execution data, axes/IOIs, block trading or market microstructure.
Extensive hands-on experience working with complex datasets, including large-scale transactional or time-series data, data preparation, feature engineering, validation and reproducible analysis.
Practical experience applying ML or LLMs in production, including content generation, 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.
Ability to design rigorous experiments, interpret results, diagnose failure modes and demonstrate measurable improvements.
Advanced degree (PhD or Master's) 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, including senior management.
Proven ownership of research delivery end-to-end, with appropriate handling of confidential data and adherence to established risk and change controls.

Preferred Qualifications

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 client targeting, recommendation or prioritization algorithms, or dynamic analytical client content with traceable sources.
Experience integrating analytical code into applications or AI agents through reusable tools, skills, plugins or MCP interfaces.
Experience leading or mentoring a team of quants or data scientists.