Equity L/S Desk Quant Analyst

Verition Fund Management LLC (“Verition”) is a multi-strategy, multi-manager hedge fund founded in 2008.

Verition focuses on global investment strategies including Global Credit, Global Convertible, Volatility & Capital Structure Arbitrage, Event-Driven Investing, Equity Long/Short & Capital Markets Trading, and Global Quantitative Trading.

We are seeking a Quant Analyst for our Fundamental Long/Short Equity business, reporting directly to the Equity L/S COO.

This is a high-impact, high-growth role at the center of how the Equity L/S platform makes decisions.

You will work on both sides of the business: quantitative analysis of investment performance and portfolio construction, and quantitative analysis applied to the strategic and operational questions that shape how the platform grows and improves.

A core strength of the role is turning open-ended questions into clear answers.

You will translate priorities from senior management and the investment teams into focused research that isolates the key drivers and points to actionable recommendations.

The work spans the business, portfolio manager, and research levels, with findings presented directly to senior stakeholders.

The individual in this role will work closely with teams across the platform.

It is well suited to someone with a strong quantitative foundation who also likes to build: hands-on, technically strong, quick to learn, and energized by working close to a fast-moving investment business.

• Build analyses, models, and tools that inform investment and business decisions across the platform.
• Analyze investment performance and portfolio properties, including process drivers of P&L and factor-model-based risk and P&L attribution.
• Execute on research ideas that improve investment and business outcomes.
• Evaluate processes and proposed changes, implement improvements, and quantify their impact.
• Support and enhance investment-process workflows, tools, and systems.
• Apply statistical methods to investment and business problems to quantify effects, test significance, and translate results into recommendations for stakeholders.
• Apply AI tools to accelerate research and analysis, and contribute to advancing AI-driven applications across the platform.