Equity Finance Quantitative Strategist — VP/SVP

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    "ExternalDescriptionStr": "<p>Equity Finance Quantitative Strategist — VP/SVP</p>\n<p>Jefferies LLC | New York, NY | Securities Finance</p>\n<p>The Opportunity</p>\n<p>Join a high-impact, entrepreneurial quant team embedded directly within the Equity Finance trading desk. You will own the full lifecycle of quantitative models — from research and prototyping through production deployment — that directly drive P&amp;L, optimize financial resource consumption, and give our clients a differentiated analytical edge. You will own the full quantitative model suite across equity swaps, securities lending, custom baskets, and prime brokerage — spanning liquidity management (ALM/MLO), valuation, counterparty risk, client analytics, factor-driven portfolio solutions, and hard-to-borrow pricing/locates.</p>\n<p>Unlike large-bank quant factories, this role offers direct partnership with senior traders, visibility to desk leadership, and the autonomy to shape the analytical direction of a rapidly growing business. You will work directly with clients on bespoke portfolio solutions and build systems that traders use every day to make real-time decisions.</p>\n<p>You will be part of a <strong>Global Quant team</strong> spanning New York and London, collaborating closely to ensure alignment on strategy, shared tooling, and state-of-the-art quantitative capabilities across regions.</p>\n<p>What You Will Own</p>\n<p><strong>Quantitative Modelling &amp; Analytics</strong></p>\n<ul>\n <li>Design and implement pre-trade optimization models for funding, liquidity risk, and tenor mismatch — driving measurable P&amp;L improvement</li>\n <li>Build factor analytics engines, custom basket construction tools, and risk decomposition frameworks for equity swap and securities finance portfolios</li>\n <li>Develop forward funding rate projection models and collateral optimization algorithms</li>\n <li>Create P&amp;L attribution, risk factor analysis, and scenario modelling across Equity Swaps and Securities Finance</li>\n <li>Liquidity Modelling.</li>\n</ul>\n<p><strong>Client-Facing Analytics &amp; Custom Solutions</strong></p>\n<ul>\n <li>Partner directly with hedge fund and institutional clients to design and optimize custom basket strategies — portfolio construction, factor tilts, and rebalancing logic</li>\n <li>Develop bespoke quantitative tools that help clients analyze their portfolio exposures, optimize execution, and manage risk</li>\n <li>Serve as a technical counterpart to clients on complex structured and systematic strategies, translating their investment objectives into quantitative implementations</li>\n <li>Build analytics that surface client flow patterns, profitability drivers, and resource consumption (balance sheet, capital, funding) at a granular level</li>\n</ul>\n<p><strong>AI, Machine Learning &amp; Intelligent Automation</strong></p>\n<ul>\n <li>Apply machine learning techniques (gradient boosting, NLP, clustering) to identify patterns in client flow, predict funding demand, and optimize inventory positioning</li>\n <li>Leverage large language models (LLMs) and generative AI to automate research workflows, extract insights from unstructured data, and build intelligent decision-support tools for the trading desk</li>\n <li>Develop AI-powered automation pipelines that eliminate manual processes — from data ingestion and reconciliation to report generation and anomaly detection</li>\n <li>Build and maintain agentic AI systems that augment trader workflows, including automated monitoring, alerting, and recommendation engines</li>\n</ul>\n<p><strong>Technology &amp; Architecture</strong></p>\n<ul>\n <li>Architect scalable, production-grade Python systems on a <strong>modern, greenfield infrastructure stack</strong> — no legacy systems, no tech debt to inherit</li>\n <li>Build on <strong>AWS-native infrastructure</strong> (S3, Redshift, Lambda, Airflow/MWAA) purpose-built for quantitative finance workloads</li>\n <li>Leverage <strong>Claude Code as the primary development environment</strong> — AI-assisted coding end-to-end, from prototyping through production deployment</li>\n <li>Access <strong>custom-built global AI agents</strong> developed by the team that provide a best-in-class developer experience: automated testing, code review, deployment pipelines, and intelligent tooling that accelerates every stage of development</li>\n <li>Build interactive dashboards and real-time analytics platforms used daily by the trading desk</li>\n <li>Own the full development lifecycle: research → prototype → production → monitoring</li>\n</ul>\n<p>Global Quant Team Partnership</p>\n<p>This role sits within a <strong>unified Global Quant team</strong> (New York + London) that operates as one unit. You will:</p>\n<ul>\n <li>Collaborate with London-based quants on shared models, analytics infrastructure, and tooling</li>\n <li>Contribute to and benefit from a shared quantitative library and reusable component ecosystem</li>\n <li>Participate in cross-regional knowledge sharing — what is built once is deployed globally</li>\n <li>Lead and define technical roadmap alongside global leadership</li>\n</ul>\n<p>What Sets You Apart</p>\n<p><strong>Required</strong></p>\n<ul>\n <li>Advanced degree (MSc/PhD) in Mathematics, Physics, Computer Science, Engineering, or quantitative discipline</li>\n <li>5+ years’ experience in a quantitative role within Equity Swaps, Prime Brokerage, Securities Finance, or a quantitative hedge fund</li>\n <li>Expert Python developer — production-quality code, not just notebooks</li>\n <li>Strong foundation in statistics, optimization, and financial mathematics</li>\n <li>Client-facing experience — comfortable presenting quantitative solutions to sophisticated institutional investors</li>\n <li>Demonstrated ability to communicate complex quantitative concepts to traders, senior management, and non-technical stakeholders</li>\n <li>Self-starter mentality — thrives with autonomy and takes ownership of outcomes</li>\n</ul>\n<p><strong>Highly Valued</strong></p>\n<ul>\n <li>Hands-on experience with machine learning in production (scikit-learn, XGBoost, PyTorch, or equivalent)</li>\n <li>Familiarity with large language models, prompt engineering, and AI-assisted development workflows (e.g., Claude Code, Copilot)</li>\n <li>Experience building automation pipelines and intelligent systems that reduce manual overhead</li>\n <li>Cloud infrastructure experience (AWS — S3, Redshift, Lambda, Airflow/MWAA)</li>\n <li>Knowledge of derivatives pricing, funding curves, or collateral management models</li>\n <li>Experience with real-time data systems, event-driven architectures, or streaming analytics</li>\n</ul>\n<p>New York, NY Full Time Salary Range of $175,000 - $300,000.&nbsp;</p>\n<p>#LI-DNI</p>",
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