Equity Finance Quantitative Strategist — VP/SVP

Jefferies•New York, NY

7d•$175,000 - $300,000•Onsite

About The Position

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&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. 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. You will be part of a Global Quant team spanning New York and London, collaborating closely to ensure alignment on strategy, shared tooling, and state-of-the-art quantitative capabilities across regions.

Requirements

Advanced degree (MSc/PhD) in Mathematics, Physics, Computer Science, Engineering, or quantitative discipline
5+ years’ experience in a quantitative role within Equity Swaps, Prime Brokerage, Securities Finance, or a quantitative hedge fund
Expert Python developer — production-quality code, not just notebooks
Strong foundation in statistics, optimization, and financial mathematics
Client-facing experience — comfortable presenting quantitative solutions to sophisticated institutional investors
Demonstrated ability to communicate complex quantitative concepts to traders, senior management, and non-technical stakeholders
Self-starter mentality — thrives with autonomy and takes ownership of outcomes

Nice To Haves

Hands-on experience with machine learning in production (scikit-learn, XGBoost, PyTorch, or equivalent)
Familiarity with large language models, prompt engineering, and AI-assisted development workflows (e.g., Claude Code, Copilot)
Experience building automation pipelines and intelligent systems that reduce manual overhead
Cloud infrastructure experience (AWS — S3, Redshift, Lambda, Airflow/MWAA)
Knowledge of derivatives pricing, funding curves, or collateral management models
Experience with real-time data systems, event-driven architectures, or streaming analytics

Responsibilities

Design and implement pre-trade optimization models for funding, liquidity risk, and tenor mismatch — driving measurable P&L improvement
Build factor analytics engines, custom basket construction tools, and risk decomposition frameworks for equity swap and securities finance portfolios
Develop forward funding rate projection models and collateral optimization algorithms
Create P&L attribution, risk factor analysis, and scenario modelling across Equity Swaps and Securities Finance
Liquidity Modelling.
Partner directly with hedge fund and institutional clients to design and optimize custom basket strategies — portfolio construction, factor tilts, and rebalancing logic
Develop bespoke quantitative tools that help clients analyze their portfolio exposures, optimize execution, and manage risk
Serve as a technical counterpart to clients on complex structured and systematic strategies, translating their investment objectives into quantitative implementations
Build analytics that surface client flow patterns, profitability drivers, and resource consumption (balance sheet, capital, funding) at a granular level
Apply machine learning techniques (gradient boosting, NLP, clustering) to identify patterns in client flow, predict funding demand, and optimize inventory positioning
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
Develop AI-powered automation pipelines that eliminate manual processes — from data ingestion and reconciliation to report generation and anomaly detection
Build and maintain agentic AI systems that augment trader workflows, including automated monitoring, alerting, and recommendation engines
Architect scalable, production-grade Python systems on a modern, greenfield infrastructure stack — no legacy systems, no tech debt to inherit
Build on AWS-native infrastructure (S3, Redshift, Lambda, Airflow/MWAA) purpose-built for quantitative finance workloads
Leverage Claude Code as the primary development environment — AI-assisted coding end-to-end, from prototyping through production deployment
Access custom-built global AI agents 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
Build interactive dashboards and real-time analytics platforms used daily by the trading desk
Own the full development lifecycle: research → prototype → production → monitoring
Collaborate with London-based quants on shared models, analytics infrastructure, and tooling
Contribute to and benefit from a shared quantitative library and reusable component ecosystem
Participate in cross-regional knowledge sharing — what is built once is deployed globally
Lead and define technical roadmap alongside global leadership