Quantitative Analyst

Quantitative Analyst (Market Intelligence & Data Products)
Location: Fully remote or hybrid in London
Reports to: Head of Enterprise Sales
Employment type: Full-time, permanent
Compensation: Competitive, dependent on experience (base + bonus)
About the Role
Our client is seeking an experienced Quantitative Analyst with strong NLP and AI expertise to build the first in-house quantitative capability within an established financial markets intelligence and data business.
This is a highly data-driven role at the intersection of quantitative finance, Natural Language Processing (NLP), machine learning and AI. A key focus will be applying modern NLP and AI techniques to proprietary, unstructured and semi-structured financial information, transforming text-rich datasets into structured intelligence, predictive signals and commercially valuable data products.
The successful candidate will combine rigorous quantitative and statistical skills with practical experience using NLP, machine learning and AI/LLM approaches to extract insight from complex financial content.
Skills & Experience
5+ years’ experience in quantitative research, quantitative analysis 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 practical experience in Natural Language Processing (NLP), machine learning and AI, particularly applied to text-based or alternative datasets.
Experience with sentiment analysis, information extraction, text classification and/or LLM-based approaches to analysing financial information.
Strong knowledge of statistical modelling, econometrics and time-series analysis.
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, NLP and AI methodologies 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 NLP and AI techniques to extract, classify and quantify information contained within large volumes of text-based financial content.
Use machine learning, sentiment analysis and LLM/AI approaches to transform unstructured information into structured, machine-readable signals and analytics.
Apply statistical and econometric techniques including time-series analysis, regression, cointegration and signal validation.
Explore how modern AI and NLP methodologies can enhance existing datasets and create new quantitative data products and signals.
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 quantitative, NLP and AI research into commercial, client-facing datasets, signals and analytics products.
Author technical research and white papers demonstrating methodologies, AI/NLP applications and findings.

Work arrangement
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