Macro Hive
Lead Quantitative AI Researcher
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Lead Quantitative AI Researcher – Macro Markets
Overview
Macro Hive is seeking a Lead Quantitative AI Researcher to build and productionise proprietary machine learning models, AI agents and systematic research frameworks that transform macroeconomic, rates, FX and cross-asset market information into differentiated investment insights.
This is a front-office role sitting at the intersection of quantitative research, artificial intelligence and macro strategy. The successful candidate will combine financial markets expertise with advanced AI and machine learning techniques to develop research signals, forecasting models and client-facing intelligence used by institutional investors globally.
The role requires an individual who can independently identify research opportunities, formulate hypotheses, build and evaluate models, and communicate actionable conclusions to both internal stakeholders and clients.
Mission
- Build and productionise Macro Hive's proprietary AI, machine learning and quantitative research capabilities to generate differentiated macro market intelligence, systematic signals and investment ideas for institutional investors.
- Develop models and workflows that combine macroeconomic data, market pricing, alternative datasets and large language models to enhance research productivity, support investment decision-making and create commercially valuable client insights.
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Responsibilities
- Develop proprietary machine-learning models for market regime identification, forecasting, event-risk analysis, sentiment measurement and market classification across macro, rates, FX and cross-asset markets.
- Design and deploy LLM, RAG and agentic workflows that support investment research, market intelligence generation and client-facing products.
- Generate novel, testable research hypotheses and convert them into systematic signals, trade frameworks and differentiated investment insights.
- Evaluate and backtest models using rigorous quantitative techniques, ensuring robustness, stability and commercial relevance.
- Establish model governance standards, including release gates, monitoring, benchmark testing, hallucination assessments and performance reviews.
- Integrate alternative datasets, economic releases, market positioning information and proprietary research inputs into scalable modelling frameworks.
- Collaborate with strategists, sales teams and engineering colleagues to translate complex market information into decision-useful outputs for institutional clients.
- Present findings, research conclusions and model outputs clearly to internal stakeholders and external investors.
- Benchmark proprietary models against commercial vendors, frontier AI models and open-source alternatives.
- Help shape Macro Hive's longer-term AI and quantitative research strategy.
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Must Have
- Advanced Python expertise with experience developing production-grade research and modelling workflows.
- Strong practical knowledge of machine learning, including classification, clustering, forecasting, time-series modelling, regime detection, feature engineering and model validation.
- Proven experience deploying LLM, RAG and agent-based systems into real-world workflows.
- Experience productionising research models beyond exploratory notebook environments.
- Strong understanding of global macro markets, including experience within FX, rates, fixed income or cross-asset investing.
- Quantitative research experience involving signal generation, backtesting and systematic investment frameworks.
- Demonstrated ability to independently generate novel market insights, research hypotheses and commercially relevant investment ideas.
- Experience working with alternative datasets and transforming unstructured information into investable signals.
- Excellent communication and presentation skills with an ability to engage effectively with research, sales teams and institutional clients.
- Strong judgement regarding model risk, governance, transparency and human-in-the-loop decision frameworks.
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