At a Glance
- Tasks: Own the research pipeline, from signal generation to portfolio analysis in a dynamic trading environment.
- Company: Join a leading firm with unmatched data and compute infrastructure for innovative research.
- Benefits: Competitive salary, collaborative culture, and access to cutting-edge technology.
- Other info: Collaborative team environment with opportunities for growth across major financial hubs.
- Why this job: Make a real impact by developing predictive models and working with live capital.
- Qualifications: 3-6 years in systematic trading, strong statistical skills, and Python proficiency.
The predicted salary is between 60000 - 80000 £ per year.
This is a systematic quant researcher role within a cross-asset trading environment spanning holding periods from hours to weeks. You will own the full research pipeline: signal generation, testing, portfolio-level analysis, and work with live capital.
The asset universe is broad: equities, futures, FX, credit, commodities, and ETF structures all feature. The problems are genuinely hard: signal decay, regime sensitivity, execution friction, and cross-asset correlation structure all matter here. You will be expected to form views, test them rigorously, and defend them.
You will have access to data and compute infrastructure at a scale very few firms can match. Research custom trading models to compete with the scale of frontier LLMs, consuming trillions of tokens of market data. Experimentation here is not constrained by tooling, it is constrained by the quality of your ideas.
Research here is a shared endeavour, not a collection of siloed books. Every researcher has full visibility into every active strategy's code. There are no black boxes, no protected territories. The expectation is that collective understanding produces better research than individual ownership.
Strategies are sized for their contribution to the portfolio as a whole, not as standalone entities. That means your work is evaluated at the system level, which rewards researchers who think carefully about covariance, capacity, and cross-strategy interaction, not just isolated backtest Sharpe.
You have 3-6 years of experience in a systematic trading environment, a hedge fund, prop trading firm, or closely related research role. You have built and shipped predictive models against real market data, not just in simulation.
Core requirements:- Strong statistical foundations: time-series analysis, factor modelling, signal research
- Python proficiency; C++ experience strongly preferred (you will be interacting with C++ day to day)
- Experience across more than one asset class, or a clear track record in one with genuine appetite to work cross-asset
- Ability to take a research idea from hypothesis to backtested strategy to production-ready code
- Comfort operating in an environment where your work is visible and subject to peer scrutiny
- You are intellectually honest about what your models do and don’t explain.
- You are more interested in understanding market structure than in protecting alpha.
- You find the idea of a shared codebase appealing rather than threatening.
London is a focus area, but realistically anywhere across the major financial hubs (NYC, Singapore, Hong Kong, Chicago).
Researcher (Data Analysis) in London employer: Augmentti
As a leading proprietary trading firm in London, we pride ourselves on fostering a dynamic and collaborative work culture that empowers our employees to innovate and excel. With access to cutting-edge GPU infrastructure and a meritocratic environment, we offer exceptional growth opportunities for those eager to develop systematic trading strategies and make a tangible impact in the financial markets. Join us to be part of a team that values curiosity and challenges the status quo, ensuring your contributions are recognised and rewarded.
StudySmarter Expert Advice🤫
We think this is how you could land Researcher (Data Analysis) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect on LinkedIn. The more people you know, the better your chances of landing that dream role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your research projects and predictive models. This is your chance to demonstrate your expertise and make a lasting impression.
✨Tip Number 3
Prepare for interviews by brushing up on your statistical foundations and coding skills. Be ready to discuss your past experiences and how they relate to the role. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s a great way to ensure your application gets noticed.
We think you need these skills to ace Researcher (Data Analysis) in London
Some tips for your application 🫡
Show Your Research Skills:Make sure to highlight your experience in systematic trading and data analysis. We want to see how you've tackled complex problems and the models you've built, so don’t hold back on the details!
Be Clear and Concise:When writing your application, keep it straightforward. We appreciate clarity, so avoid jargon unless it's necessary. Remember, we’re looking for someone who can communicate their ideas effectively.
Demonstrate Team Spirit:Since we operate as a collaborative team, mention any experiences where you’ve worked closely with others on research projects. Show us that you value shared knowledge and can thrive in a transparent environment.
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Augmentti
✨Know Your Data Inside Out
Make sure you’re well-versed in the data you’ll be working with. Brush up on your statistical foundations, especially time-series analysis and factor modelling. Be ready to discuss how you've built predictive models using real market data, not just simulations.
✨Showcase Your Coding Skills
Since Python proficiency is a must, prepare to demonstrate your coding skills during the interview. Have examples ready that showcase your ability to take a research idea from hypothesis to production-ready code. If you have experience with C++, even better—be prepared to discuss how you’ve interacted with it in your previous roles.
✨Emphasise Collaboration
This role values shared understanding over individual ownership. Be ready to talk about your experiences working in a team environment where peer scrutiny is the norm. Highlight instances where collective efforts led to better research outcomes and how you contributed to that process.
✨Be Ready for Tough Questions
Expect to face challenging questions about signal decay, regime sensitivity, and cross-asset correlation structures. Prepare to defend your views and methodologies rigorously. Show that you’re intellectually honest about your models and are more interested in understanding market structure than just protecting alpha.
