At a Glance
- Tasks: Develop alpha-generating signals and pricing models for ETFs and index strategies.
- Company: Dynamic quantitative trading firm focused on research-driven strategies.
- Benefits: Collaborative team, minimal meetings, and access to top-notch infrastructure.
- Other info: Join a small, senior team where your work truly matters.
- Why this job: Your research directly influences trading decisions in a fast-paced environment.
- Qualifications: PhD or equivalent experience in quantitative fields; strong programming skills required.
The predicted salary is between 60000 - 80000 £ per year.
My client is a research-driven quantitative trading firm operating continuously across global markets. Their edge comes entirely from systematic models, not intuition, and their researchers own the full stack: hypothesis, signal, backtest, implementation, and live trading. If your work is good, it goes live. If it doesn't perform, you find out why and fix it.
What you'll be working on:
- Developing alpha-generating signals and pricing models for ETF and index-related strategies
- Analysing market microstructure: quote dynamics, liquidity, intraday flows, and passive rebalancing mechanics
- Working with large-scale tick data to identify and exploit structural inefficiencies in exchange-traded products
- Improving execution models and understanding cross-asset hedging relationships
What they are looking for:
- Strong quantitative background (PhD or equivalent research experience in mathematics, statistics, physics, computer science, or a related field)
- 3+ years of applied experience in a systematic trading or quantitative research environment
- Comfort with the full research lifecycle, from data wrangling to live deployment
- Solid programming skills in Python and/or C++; experience with large financial datasets
- Genuine understanding of ETF mechanics, index arbitrage, or market microstructure (not just passing familiarity)
What this is not:
- A sales or structured products role
- A "quant" title with a trader making the calls above you
- A firm where research output gets lost in committee
What you get:
- A small, senior, collaborative team where your research directly determines what they trade. Minimal meetings. High standards. The infrastructure and data to do the work properly.
If you have a meaningful track record in systematic ETF research or market making and want to understand what they're building, reach out.
Quantitative Researcher (ETFs) in London employer: Augmentti
As a leading research-driven quantitative trading firm, we pride ourselves on fostering a collaborative and innovative work culture where your contributions directly impact our trading strategies. With minimal meetings and a focus on high standards, we provide the infrastructure and data necessary for you to excel in your role as a Quantitative Researcher, while also offering ample opportunities for professional growth and development in the dynamic field of systematic trading.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher (ETFs) in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the quantitative trading space 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 best projects, especially those related to ETF strategies or market microstructure. This gives potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your programming skills in Python or C++. Be ready to discuss your past research and how it relates to the role. Practice makes perfect!
✨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, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Quantitative Researcher (ETFs) in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your strong quantitative background and any relevant experience in systematic trading or quantitative research. We want to see how your skills align with the role, so don’t hold back!
Be Specific About Your Experience:When detailing your past work, focus on the full research lifecycle you’ve been involved in. We love candidates who can demonstrate their journey from hypothesis to live deployment, so give us the juicy details!
Demonstrate Your Understanding:We’re looking for a genuine understanding of ETF mechanics and market microstructure. Use your application to show us that you know your stuff and can think critically about these concepts.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Augmentti
✨Know Your Quantitative Stuff
Make sure you brush up on your quantitative skills and be ready to discuss your research experience in detail. They’ll want to hear about your work with systematic models, so prepare examples that showcase your understanding of the full research lifecycle.
✨Get Familiar with ETFs and Market Microstructure
Dive deep into ETF mechanics and market microstructure concepts. Be prepared to discuss how you’ve applied this knowledge in past roles, especially regarding liquidity and intraday flows. Showing genuine understanding will set you apart from other candidates.
✨Show Off Your Programming Skills
Since solid programming skills in Python and/or C++ are crucial, be ready to talk about specific projects where you used these languages. If possible, bring along code snippets or examples of how you’ve worked with large financial datasets to solve problems.
✨Be Ready for Problem-Solving Questions
Expect to face questions that test your analytical thinking and problem-solving abilities. They might present you with a hypothetical scenario related to alpha-generating signals or execution models. Practice articulating your thought process clearly and logically.
