Quant Strategist

A top-tier proprietary trading firm has engaged us to find a Quant Strategist for their quantitative analytics group. The firm has been active across global markets for many years, trading its own capital across a broad mix of asset classes. Decisions are made fast, ownership sits with the people closest to the trade, and the bar for technical talent is high.

This is a front-office seat where quantitative finance, applied AI, and software meet. The team is building an internal intelligence platform that pulls together data, analytics, and research so that traders, researchers, and risk teams can get from a question to a decision faster. Your charter is to make that platform something the desks cannot work without

In practice, that means sitting with trading desks to learn how they actually operate, translating what they need into concrete quant problems, building the models and AI capabilities to solve them, and working alongside engineers to ship those capabilities to production. The work spans the full research arc, sourcing data, constructing signals, testing strategies, simulating PnL, and stress-testing risk across multiple desks and asset classes.

The end goal is direct: better, faster, AI-assisted trading and research that shows up in revenue.

• Embed with desks and other teams to surface the use cases where the platform can create real ed
• ge.
  
  Strike the right division of labor between what the AI handles autonomously and what should run through purpose-built, well-defined tooling, then build
• it.
  
  Take ideas end to end: data access, signal design, strategy evaluation, PnL and scenario analysis, and the tests around th
• em.
  
  Write production-grade quant code and libraries, including components meant to be invoked and interpreted by AI systems, with clean interfaces and documentati
• on.
  
  Sharpen how the platform interprets market data and produces reliable, context-aware output across products.
• Track usage, find the weak spots, and keep raising the platform's value to the people relying on it
• Serve as the first point of quant support for users, feeding what you learn back into the roadmap.

• A strong technical foundation — quantitative finance, financial engineering, applied math, statistics, physics, CS, or similar.
• Roughly 5–8 years in a front-office quant, strategist, or research role, ideally touching more than one asset class
• Real fluency with markets, pricing and risk methods, and PnL attribution.
• A track record contributing to analytics tools or platforms used by traders and researchers.
• Hands-on work in signals, backtesting, or systematic strategy development.
• Strong Python and comfort with Git-based collaborative development.
• Exposure to AI methods in a quant context is a meaningful plus; building AI agents and more
• The communication skills to work shoulder to shoulder with desks and engineers and bridge the two.
• Curiosity, range, and the appetite to learn fast in a market that is moving quickly