Junior 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.

This role is designed for a recent college graduate or graduate student (Masters/PhD) with strong technical and quantitative skills who wants to apply them in financial markets. The core skills required are: the ability to write production-quality code, hands-on experience building with AI tooling, comfort working with real-world data, and rigorous quantitative thinking. Graduate candidates should bring deeper mathematical foundations and research rigor.

Responsibilities:

• Design, build, and maintain quantitative tools and models used by the desk—pricing engines, curve fitting tools, scenario analysis frameworks, and interactive dashboards. Your code ships to production and gets used daily.
• Identify manual workflows and replace them with automated, AI-augmented pipelines. Build LLM-powered tools for filing extraction, earnings call analysis, data aggregation, and research acceleration. You will be the person who makes the desk faster and more systematic.
• As you develop market knowledge, surface trade ideas and risk management insights through the tools and models you’ve built. Translate quantitative analysis into actionable conclusions.
• Apply rigorous data science practices to everything you build: proper experiment design, out-of-sample validation, reproducible pipelines, and version-controlled code. The desk relies on the integrity of its quantitative work.

Qualifications:

• Bachelor’s, Masters, or PhD (2026) in computer science, applied math, statistics, physics, engineering, financial engineering, or a related quantitative field. Graduate candidates should have thesis or research work involving real-world data and computation.
• Coding Proficient in Python with demonstrated ability to build end-to-end projects. Comfortable with data manipulation, APIs, and libraries. Writes modular, version-controlled code—not stream-of-consciousness notebooks.
• Hands-on experience building with AI tooling: LLM-powered agents, RAG pipelines, NLP applications, or AI-assisted development. Must demonstrate using AI as a building material for real projects, not just as a consumer.
• Strong foundation in probability, statistics, and ideally stochastic processes or optimization. Comfort with mathematical modeling and the ability to pick up new quantitative frameworks quickly. Graduate candidates should demonstrate deeper command of these areas through thesis work or coursework.
• At least one substantial project, thesis, or published research demonstrating quantitative thinking applied to a real-world problem. Should demonstrate how you think and build, not just what tools you know.
• Development Practices Familiarity with Git/GitHub, collaborative workflows, parameter versioning, and reproducible research. Evidence of writing code meant to be read, maintained, and extended by others.