Front-Office Quant AI Strategist: Platform & Insight

Trading Interview; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

DRW is expanding its quantitative analytics team and seeks a Quantitative AI Strategist to join its front-office platform development. You will bridge AI, data science, and finance to enable traders and researchers to convert questions into actionable insights.

The role spans signal generation, backtesting, risk analysis, and research analytics across asset classes, with a focus on production-quality AI capabilities and close collaboration with trading desks and software engineers.

Qualifications

• Background in quantitative finance, financial engineering, applied mathematics, statistics, physics, or computer science.
• 3–7 years in a front-office quant, strategist, or quantitative research role across multiple asset classes.
• Strong programming in Python with familiarity in Git and collaborative development.
• Experience building analytics platforms or tools used by traders and researchers.

Responsibilities

• Prototype end-to-end quantitative workflows from data retrieval to strategy evaluation and risk analysis.
• Define AI interactions with data sources and analytics libraries, delivering production-ready capabilities.
• Enhance AI-driven market reasoning and contextual analysis across products and markets.
• Evaluate platform usage and drive improvements to trading and research workflows.
• Engage with trading desks, risk managers, and researchers to identify high-value use cases.
• Identify new AI capabilities and act as the first line of quantitative support for platform users.

Skills

Python

AI

Backtesting

Signal generation

Financial engineering

Git

Communication

Problem solving

Education

Quantitative degree

Tools

Git

Job description

DRW is expanding its quantitative analytics team and seeks a Quantitative AI Strategist to join its front-office platform development. You will bridge AI, data science, and finance to enable traders and researchers to convert questions into actionable insights.

The role spans signal generation, backtesting, risk analysis, and research analytics across asset classes, with a focus on production-quality AI capabilities and close collaboration with trading desks and software engineers.