Permutable
Graduate Quantitative Researcher
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About Permutable
Permutable is a UK-based artificial intelligence and market intelligence company building data and quantitative products for global financial markets. We transform large volumes of multilingual news, economic, market and alternative data into structured signals that can be researched, tested and used by institutional investors, trading desks and other market participants.
Our work sits at the intersection of quantitative finance, alternative data and AI. We develop proprietary datasets and systematic signals across areas including commodities and global macro, with the objective of turning complex real-world information into measurable and investable market intelligence.
About the role
Permutable is looking for a talented Graduate Quantitative Researcher to help us discover, develop and backtest new systematic trading strategies using our proprietary datasets.
This is a hands-on research role for someone who enjoys markets, statistics and programming. You will take ideas from an initial hypothesis, test whether our data contains genuine predictive information, and help turn successful research into robust quantitative strategies and products.
What you'll do
- Research new systematic trading strategies using Permutable's proprietary datasets.
- Backtest our existing and newly developed data to identify predictive signals and potential sources of alpha.
- Analyse signals across different markets, assets, regimes and time horizons.
- Build and improve robust Python research and backtesting tools.
- Test techniques including normalisation, ranking, Z-scores, signal smoothing, regime filters and portfolio construction.
- Evaluate strategies using returns, volatility, Sharpe ratio, drawdown, turnover, correlation, capacity and transaction costs.
- Perform out-of-sample testing, walk-forward analysis and robustness checks to reduce overfitting and false discoveries.
- Investigate combinations of alternative data, market data, fundamental information and AI-derived signals.
- Research position sizing, portfolio construction and risk-management approaches.
- Clearly document what was tested, why a strategy appears to work, and where it fails.
- Work with engineering and product teams to move successful research towards production and client delivery.
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What we're looking for
- Bachelor's or Master's degree in Mathematics, Statistics, Physics, Computer Science, Engineering, Economics, Finance or another highly quantitative subject.
- Strong Python skills, particularly pandas, NumPy and scientific/data-analysis libraries.
- Good understanding of statistics, probability and time-series analysis.
- Ability to work with large datasets and independently investigate patterns in data.
- A genuine interest in financial markets and systematic trading.
- Understanding of concepts such as returns, volatility, correlation, Sharpe ratio and drawdown.
- Strong analytical thinking and a willingness to challenge results rather than simply optimise a backtest.
- Ability to communicate quantitative research clearly to both technical and non-technical colleagues.
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Nice to have
Experience with any of the following would be useful, but isn't required:
- Quantitative finance, systematic trading or academic research projects.
- Commodities, futures, rates or FX.
- Machine learning applied to financial time series.
- Alternative data, NLP or LLM-derived signals.
- Portfolio optimisation and risk models.
- Git, SQL and cloud-based data environments.
- Personal quantitative research, trading competitions or other evidence of independently testing ideas with data.
What makes the role interesting
You won't simply maintain existing models. You'll be given access to proprietary datasets and asked questions such as:
- Does this dataset contain tradable information?
- Which markets does it predict?
- At what horizon does the signal work?
- Is the result robust, or are we overfitting? Can we turn it into a strategy that survives transaction costs and out-of-sample testing?
Successful research can ultimately contribute to quantitative research and data products used by institutional clients.
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