We are seeking an exceptional Macro Quant Researcher to join a highly sophisticated systematic investment platform. This role offers the opportunity to conduct original quantitative research and develop systematic investment strategies across global macro markets.

You will work at the intersection of financial markets, statistics, machine learning and technology, using large and complex datasets to identify persistent market opportunities. The role provides significant scope to pursue independent research ideas while collaborating with experienced quantitative researchers, portfolio managers and engineers.

• Conduct quantitative research into systematic trading opportunities across global macro markets.
• Research, design and develop predictive signals and systematic investment strategies.
• Analyse large-scale financial, economic and alternative datasets to uncover patterns and market relationships.
• Develop statistical and machine learning models for forecasting asset returns, market behaviour and risk.
• Build rigorous research frameworks for testing hypotheses and evaluating investment signals.
• Perform backtesting, validation and robustness analysis to distinguish genuine predictive relationships from noise.
• Research cross-asset relationships across areas such as equities, fixed income, FX and commodities.
• Investigate macroeconomic, fundamental, positioning, sentiment and market-based datasets as potential sources of alpha.
• Improve existing models and identify new opportunities through continuous empirical research.
• Work closely with quantitative developers and investment professionals to translate research into systematic strategies.
• Monitor live strategies and investigate changes in signal behaviour, market regimes and performance.
• Communicate research findings clearly, including the assumptions, risks and limitations behind individual models.

The role will suit someone interested in applying rigorous quantitative methods to real-world financial markets. Potential research areas may include:

• Systematic macro
• Cross-asset research
• Time-series and cross-sectional modelling
• Return forecasting
• Statistical signal generation
• Machine learning
• Alternative data
• Macroeconomic modelling
• Market regime analysis
• Portfolio construction
• Risk modelling
• Execution and transaction-cost considerations

We are looking for candidates with exceptionally strong quantitative and analytical ability. You may come from quantitative finance, mathematics, statistics, computer science, physics, engineering or another highly technical research discipline.

Ideal candidates will demonstrate:

• Advanced quantitative training, ideally an MSc or PhD in a highly mathematical or computational discipline.
• Strong knowledge of probability, statistics and statistical modelling.
• Experience conducting independent, hypothesis-driven research.
• Strong programming ability, particularly in Python or another research-oriented language.
• Experience working with large, noisy and complex datasets.
• Understanding of machine learning techniques and their application to predictive modelling.
• Strong awareness of overfitting, selection bias, data leakage and the challenges associated with financial time-series research.
• Ability to develop ideas from initial hypothesis through research, validation and implementation.
• Intellectual curiosity and a genuine interest in understanding how financial markets behave.
• Strong communication skills and the ability to explain complex quantitative research clearly.

Experience in one or more of the following would be advantageous:

• Systematic macro or quantitative trading
• Alpha research
• Signal generation
• Time-series forecasting
• Econometrics
• Machine learning
• Portfolio optimisation
• Cross-asset modelling
• Financial or alternative data research
• Fixed income, rates, FX, commodities or equity index markets

Candidates do not necessarily need to come from an existing hedge fund or proprietary trading environment. Exceptional researchers from academia, technology or other highly quantitative fields who can demonstrate strong statistical research and programming ability may also be considered.