Drweng1mo ago

Quantitative AI Strategist

London · (london)mid

Data ScienceOtherQuantitative StrategistQuantitative Ai Strategist

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Quick Summary

Key Responsibilities

Prototype and validate quantitative workflows end-to-end — from data retrieval and signal construction through to strategy evaluation, PnL simulation, testing,

Requirements Summary

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,

Technical Tools

Data ScienceOtherQuantitative StrategistQuantitative Ai Strategist

Responsibilities

~1 min read

→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.

Requirements

~1 min read

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.