Quantitative AI Strategist

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it’s how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

We are seeking a Quantitative AI Strategist to join our quantitative analytics team. This is a front-office role at the intersection of quantitative finance, AI, and product development — focused on building and evolving the firm’s AI-powered research and analytics platform. The platform helps traders, researchers, analysts, and risk managers move from questions to actionable insight by unifying analytics, data, and research. Your job is to make it indispensable — by working directly with trading desks to understand their workflows, building the quantitative and AI capabilities they need to generate better ideas and make better decisions, and partnering with software engineers to deliver them at production quality.

You will have broad exposure across asset classes, desks, and problem types — from signal generation and backtesting to risk analysis and research analytics — while working at the frontier of applying AI to quantitative finance. The ultimate goal is to help the firm generate more revenue through AI-assisted trading and research.

The ideal candidate will be able to:

• Work directly with trading desks across asset classes and other stakeholders across the firm to identify high-value use cases for the platform.
• Determine the right balance between AI autonomy and structured tooling — deciding what the AI should reason through on its own, what instructions and domain knowledge it needs, and what purpose-built code it should call — and build accordingly.
• Work with front-office stakeholders to turn desk needs into well-defined quantitative problems/workflows, and collaborate with technology teams and quantitative researchers to deliver solutions.

Key Responsibilities

• Prototype and validate quantitative workflows end-to-end — from data retrieval and signal construction through to strategy evaluation, PnL simulation, testing, and risk/scenario analysis — while defining how the AI should interact with data sources, analytics libraries, desk-specific tools, etc., and work with engineers to deliver them as production platform capabilities.
• Write high-quality platform code and quantitative libraries — including code designed to be called and understood by AI, with clear interfaces, documentation, and instructions to AI.
• Enhance the platform’s ability to reason about markets, interpret financial data, and produce reliable, contextually aware analysis across products and markets.
• Continuously evaluate how the platform is used, identify where it excels and where it falls short, and drive improvements that deliver measurable value to trading and research workflows.
• Engage with stakeholders across the firm — trading desks, risk management, researchers, new joiners, and others — to discover emerging use cases and adapt the platform’s capabilities accordingly.
• Proactively identify new use cases and capabilities as AI technology evolves.
• Act as the first line of quantitative support for platform users — diagnosing issues, feeding insights back into platform development, and ensuring a high-quality user experience.

Qualification and Experience

• Background in quantitative finance, financial engineering, applied mathematics, statistics, physics, computer science, or a related technical field.
• 3–7 years’ experience in a front-office quant, strategist, or quantitative research role, ideally with exposure to multiple asset classes.
• Solid understanding of financial markets, pricing/risk methodologies, and PnL attribution.
• Experience building or contributing to internal analytics platforms or tools used by traders and researchers.
• Experience with signal generation, backtesting, or systematic strategy development.
• Strong programming skills in Python. Familiarity with Git and collaborative development workflows.
• Familiarity with AI technologies and their application to quantitative workflows is a strong plus.
• Experience building AI agents is a strong plus.
• Excellent communication skills — able to engage directly with trading desks to understand their needs, formalize them into quantitative specifications, and collaborate effectively with software engineers.
• Strong problem-solving ability, intellectual curiosity, and comfort working across team boundaries in a fast-paced trading environment.
• Strong ability to quickly learn and adapt to new technologies — particularly important given the rapid pace of development in AI.