At a Glance
- Tasks: Research and develop systematic trading strategies using cutting-edge datasets.
- Company: Join a pioneering AI and market intelligence firm transforming financial data.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic role with access to proprietary datasets and real-world applications.
- Why this job: Make a real impact in finance by turning complex data into actionable insights.
- Qualifications: Degree in a quantitative field and strong Python programming skills required.
The predicted salary is between 29700 - 36300 £ per year.
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.
- 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, Num Py 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.
- Nice to have
- Experience with any of the following would be useful, but isn't required:
- Quantitative finance, systematic trading or academic research projects.
- 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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Graduate Quantitative Researcher employer: Permutable
At Permutable AI, we pride ourselves on being an excellent employer by fostering a dynamic and innovative work culture where every team member has the opportunity to make a real impact. As a Senior Platform Engineer, you'll enjoy significant ownership of our cutting-edge architecture while collaborating closely with our founder and engineering team in a hybrid work environment that promotes flexibility and work-life balance. With a focus on employee growth and the chance to work with advanced AI technologies, we offer a unique opportunity for those looking to thrive in a fast-paced startup atmosphere.
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We think this is how you could land Graduate Quantitative Researcher
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Graduate Quantitative Researcher
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Permutable, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Permutable. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Permutable
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Permutable!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
