At a Glance
- Tasks: Develop and refine trading strategies while conducting quantitative research in a dynamic environment.
- Company: Join a small, ambitious team at a leading trading firm in London.
- Benefits: Enjoy a collaborative culture with opportunities for ownership and continuous learning.
- Other info: Fast-paced environment with excellent career growth and exposure to experienced professionals.
- Why this job: Make a direct impact on trading performance and shape innovative research strategies.
- Qualifications: 4+ years in quantitative research, strong Python or R skills, and equities market experience.
The predicted salary is between 60000 - 80000 £ per year.
This role offers the opportunity to play a key part in building an internal trading desk, acting as the analytical engine behind trading strategies, algorithms and product innovation. You will work across systematic trading, risk management and pricing, helping to shape how the trading platform operates and scales over time. The position suits someone who enjoys combining rigorous quantitative research with hands-on development in a fast-moving, equities-focused environment.
Responsibilities
- Develop and refine systematic trading strategies within a mid-frequency, equities-focused environment.
- Conduct quantitative research to support and enhance existing trading strategies and identify new opportunities.
- Build and improve risk management processes and tooling to support systematic strategies.
- Design and implement research infrastructure, including robust back-testing frameworks and scalable data pipelines.
- Contribute to inventory management, hedging and pricing strategies to optimise trading performance.
- Help expand modelling capabilities across additional asset classes over time.
- Collaborate closely with trading and technology teams to translate research into production-ready models and tools.
- Monitor and evaluate model performance, making data-driven improvements and adjustments as needed.
Essential Skills
- At least 4 years of experience in a quantitative research role, ideally within systematic trading, market making, or a quantitative fund.
- Proven experience in feature engineering and predictive model building.
- Solid understanding of risk management with a track record of working on systematic trading strategies.
- Strong experience in equities markets.
- Strong development skills in Python or R.
- Working understanding of risk management and optimisation software.
- Familiarity with market making or trading technology stacks.
- Ability to design and implement research infrastructure such as back-testing frameworks and data pipelines.
- Strong quantitative and analytical skills with a rigorous, data-driven approach to problem solving.
Additional Skills & Qualifications
- Experience in a retail flow, neo-broker, or market making environment.
- Advanced degree in mathematics, statistics, physics, or a related STEM field.
- Experience with market microstructure is a strong plus.
- Comfort working in a small, fast-moving team where you are building systems and processes from the ground up.
- Ability to communicate complex quantitative concepts clearly to non-specialists.
- Proactive mindset with the ability to take ownership of projects from idea to implementation.
Why Work Here?
You will join a small, ambitious team where your work has a direct and visible impact on trading performance and the evolution of the platform. The environment encourages ownership, experimentation and continuous learning, giving you the freedom to shape research infrastructure and strategies from an early stage. You will collaborate closely with experienced professionals across trading and technology, gaining broad exposure rather than being confined to a narrow remit. This is an opportunity to work in a fast-moving setting that values initiative, intellectual curiosity and practical problem solving.
Work Environment
You will work in a modern, technology-driven trading environment focused on systematic, mid-frequency equities strategies. The role is highly collaborative, involving close interaction with traders, engineers and other quantitative researchers. You will use a contemporary quantitative technology stack, with Python and R at its core, supported by risk management and optimisation software, and trading or market making technology platforms. The setting is fast-paced and iterative, with an emphasis on building robust research infrastructure, including back-testing frameworks and scalable data pipelines. The team operates in a professional office environment where the focus is on delivering high-quality research and production-ready tools.
Location
London, UK
Quantitative Researcher - Equities in London employer: Teksystems
Join a dynamic and innovative team in London as a Quantitative Researcher, where your contributions will directly influence trading performance and platform evolution. Our collaborative work culture fosters ownership, experimentation, and continuous learning, providing you with the opportunity to shape research infrastructure and strategies from the ground up. With access to cutting-edge technology and a focus on professional growth, this role offers a unique chance to thrive in a fast-paced, intellectually stimulating environment.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher - Equities in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the quantitative research field on LinkedIn or at industry events. A personal connection can often get your foot in the door faster than a CV.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your quantitative research projects, algorithms, and any trading strategies you've developed. This gives potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with Python or R, and have examples of how you've tackled complex problems in systematic trading.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team.
We think you need these skills to ace Quantitative Researcher - Equities in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Quantitative Researcher role. Highlight your experience in systematic trading, risk management, and any relevant quantitative research you've done. We want to see how your skills align with what we're looking for!
Showcase Your Skills:Don’t just list your skills; demonstrate them! Use specific examples of how you've applied Python or R in your previous roles, especially in building predictive models or back-testing frameworks. This will help us see your hands-on experience.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Explain why you're passionate about quantitative research and how you can contribute to our trading desk. Be sure to mention any unique experiences that set you apart from other candidates.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Teksystems
✨Know Your Quantitative Stuff
Make sure you brush up on your quantitative research skills, especially in systematic trading and risk management. Be ready to discuss your experience with feature engineering and predictive model building, as well as any specific projects you've worked on that relate to equities markets.
✨Show Off Your Coding Skills
Since Python and R are key tools for this role, be prepared to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code and be ready to explain your thought process as you go.
✨Understand the Trading Environment
Familiarise yourself with the fast-paced nature of trading environments. Be ready to discuss how you would approach building robust research infrastructure and back-testing frameworks, and think about how you can contribute to optimising trading performance.
✨Communicate Clearly
You’ll need to explain complex quantitative concepts to non-specialists, so practice simplifying your explanations. Think of examples where you’ve successfully communicated technical ideas to a broader audience, as this will show your ability to collaborate effectively within a team.
