Quantitative Developer

Quantitative Developer (Market Intelligence & Data Products)

Location: Fully remote or hybrid in London

Reports to: Head of Enterprise Sales

Employment type: Full-time, permanent

C ompensation: Competitive, dependent on experience (base + bonus)

About the Role

Our client is seeking an experienced Quantitative Developer / Quantitative Analyst to build the first in-house quantitative capability within an established financial markets intelligence and data business.

Skills & Experience

• 5+ years’ experience in quantitative research, quantitative development or financial data science, ideally within a hedge fund, investment bank or similar financial markets environment.
• Alternatively, relevant experience within a fintech, financial-data or AI business.
• Proven experience deriving actionable or tradable signals from unstructured or semi-structured financial data .
• Strong knowledge of statistical modelling, econometrics and time-series analysis.
• Practical experience with sentiment analysis, NLP, machine learning and AI/LLM approaches .
• Strong programming skills, with Python preferred .
• Understanding of back-testing, statistical significance and out-of-sample validation.
• Experience with financial markets data; macro, fixed income, FX, commodities or credit experience is particularly relevant.
• Strong quantitative academic background, ideally mathematics, statistics, physics, computer science, engineering or econometrics.
• Ability to communicate complex quantitative concepts to both technical and commercial audiences.

Key Responsibilities

• Analyse proprietary historical and unstructured datasets to identify correlations with asset prices and potential tradable or predictive signals .
• Apply statistical and econometric techniques including time-series analysis, regression, cointegration and signal validation.
• Use NLP, machine learning, sentiment analysis and LLM/AI techniques to extract structured insights from text-based financial content.
• Develop robust back-testing and out-of-sample validation frameworks.
• Improve the machine-readability, metadata and governance of proprietary datasets.
• Build reproducible research pipelines and establish quantitative data standards and best practices.
• Translate research into commercial, client-facing datasets, signals and analytics products .
• Author technical research and white papers demonstrating methodologies and findings.