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.