At a Glance
- Tasks: Conduct cutting-edge research in a dynamic trading environment across various asset classes.
- Company: Join a leading firm with unmatched data and compute infrastructure.
- Benefits: Competitive salary, collaborative culture, and opportunities for professional growth.
- Other info: Work in a transparent team environment that values collective understanding.
- Why this job: Make a real impact by developing innovative trading models and strategies.
- 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
The right mindset:
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).
Researchers 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 Researchers 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 models. This is your chance to demonstrate your expertise in systematic trading 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 challenges mentioned in the job description.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Researchers in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience in systematic trading and any predictive models you've built. We want to see how you've tackled real market data, 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 complex ideas simply.
Demonstrate Team Spirit:Since we value collaboration, share examples of how you’ve worked with others in research settings. Show us that you’re not just a lone wolf but someone who thrives in a shared environment.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re keen on joining the StudySmarter team!
How to prepare for a job interview at Augmentti
✨Know Your Models Inside Out
Make sure you can explain your predictive models in detail. Be ready to discuss how you've built and tested them against real market data, as well as the statistical foundations behind your work. This will show that you not only understand your models but can also defend their effectiveness.
✨Embrace Collaboration
Since the role emphasises shared research, be prepared to discuss how you’ve worked in a team environment before. Highlight experiences where collective understanding led to better outcomes, and express your enthusiasm for contributing to a shared codebase rather than working in isolation.
✨Demonstrate Cross-Asset Knowledge
Showcase your experience across different asset classes or a deep understanding of one. Be ready to discuss how you approach problems like signal decay and execution friction in various contexts. This will demonstrate your versatility and readiness to tackle the challenges of a cross-asset trading environment.
✨Prepare for Technical Questions
Brush up on your Python and C++ skills, as you'll likely face technical questions during the interview. Be ready to solve problems on the spot or discuss your coding practices. This will help you stand out as someone who is not just theoretically knowledgeable but also practically skilled.
