TrueNorth®
Quantitative Developer
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Quantitative Developer (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 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.
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Key Responsibilities
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- 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.
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